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is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Kim%2C+H+K&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Kim%2C+H+K&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Kim%2C+H+K&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.04524">arXiv:2408.04524</a> <span> [<a href="https://arxiv.org/pdf/2408.04524">pdf</a>, <a href="https://arxiv.org/format/2408.04524">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Field Testing and Detection of Camera Interference for Autonomous Driving </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+K+B">Ki Beom Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.04524v1-abstract-short" style="display: inline;"> In recent advancements in connected and autonomous vehicles (CAVs), automotive ethernet has emerged as a critical technology for in-vehicle networks (IVNs), superseding traditional protocols like the CAN due to its superior bandwidth and data transmission capabilities. This study explores the detection of camera interference attacks (CIA) within an automotive ethernet-driven environment using a no… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04524v1-abstract-full').style.display = 'inline'; document.getElementById('2408.04524v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.04524v1-abstract-full" style="display: none;"> In recent advancements in connected and autonomous vehicles (CAVs), automotive ethernet has emerged as a critical technology for in-vehicle networks (IVNs), superseding traditional protocols like the CAN due to its superior bandwidth and data transmission capabilities. This study explores the detection of camera interference attacks (CIA) within an automotive ethernet-driven environment using a novel GRU-based IDS. Leveraging a sliding-window data preprocessing technique, our IDS effectively analyzes packet length sequences to differentiate between normal and anomalous data transmissions. Experimental evaluations conducted on a commercial car equipped with H.264 encoding and fragmentation unit-A (FU-A) demonstrated high detection accuracy, achieving an AUC of 0.9982 and a true positive rate of 0.99 with a window size of 255. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04524v1-abstract-full').style.display = 'none'; document.getElementById('2408.04524v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 15 figures, 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 25th World Conference on Information Security Application (WISA2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.04506">arXiv:2408.04506</a> <span> [<a href="https://arxiv.org/pdf/2408.04506">pdf</a>, <a href="https://arxiv.org/format/2408.04506">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Who ruins the game?: unveiling cheating players in the "Battlefield" game </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+D+Y">Dong Young Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.04506v1-abstract-short" style="display: inline;"> The "Battlefield" online game is well-known for its large-scale multiplayer capabilities and unique gaming features, including various vehicle controls. However, these features make the game a major target for cheating, significantly detracting from the gaming experience. This study analyzes user behavior in cheating play in the popular online game, the "Battlefield", using statistical methods. We… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04506v1-abstract-full').style.display = 'inline'; document.getElementById('2408.04506v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.04506v1-abstract-full" style="display: none;"> The "Battlefield" online game is well-known for its large-scale multiplayer capabilities and unique gaming features, including various vehicle controls. However, these features make the game a major target for cheating, significantly detracting from the gaming experience. This study analyzes user behavior in cheating play in the popular online game, the "Battlefield", using statistical methods. We aim to provide comprehensive insights into cheating players through an extensive analysis of over 44,000 reported cheating incidents collected via the "Game-tools API". Our methodology includes detailed statistical analyses such as calculating basic statistics of key variables, correlation analysis, and visualizations using histograms, box plots, and scatter plots. Our findings emphasize the importance of adaptive, data-driven approaches to prevent cheating plays in online games. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04506v1-abstract-full').style.display = 'none'; document.getElementById('2408.04506v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 5 figures, 2 tables; accepted to the 25th World Conference on Information Security Applications (WISA 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.12721">arXiv:2406.12721</a> <span> [<a href="https://arxiv.org/pdf/2406.12721">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Sound event detection based on auxiliary decoder and maximum probability aggregation for DCASE Challenge 2024 Task 4 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Son%2C+S+W">Sang Won Son</a>, <a href="/search/cs?searchtype=author&query=Park%2C+J">Jongyeon Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Hong Kook Kim</a>, <a href="/search/cs?searchtype=author&query=Vesal%2C+S">Sulaiman Vesal</a>, <a href="/search/cs?searchtype=author&query=Lim%2C+J+E">Jeong Eun Lim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.12721v2-abstract-short" style="display: inline;"> In this report, we propose three novel methods for developing a sound event detection (SED) model for the DCASE 2024 Challenge Task 4. First, we propose an auxiliary decoder attached to the final convolutional block to improve feature extraction capabilities while reducing dependency on embeddings from pre-trained large models. The proposed auxiliary decoder operates independently from the main de… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12721v2-abstract-full').style.display = 'inline'; document.getElementById('2406.12721v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.12721v2-abstract-full" style="display: none;"> In this report, we propose three novel methods for developing a sound event detection (SED) model for the DCASE 2024 Challenge Task 4. First, we propose an auxiliary decoder attached to the final convolutional block to improve feature extraction capabilities while reducing dependency on embeddings from pre-trained large models. The proposed auxiliary decoder operates independently from the main decoder, enhancing performance of the convolutional block during the initial training stages by assigning a different weight strategy between main and auxiliary decoder losses. Next, to address the time interval issue between the DESED and MAESTRO datasets, we propose maximum probability aggregation (MPA) during the training step. The proposed MPA method enables the model's output to be aligned with soft labels of 1 s in the MAESTRO dataset. Finally, we propose a multi-channel input feature that employs various versions of logmel and MFCC features to generate time-frequency pattern. The experimental results demonstrate the efficacy of these proposed methods in a view of improving SED performance by achieving a balanced enhancement across different datasets and label types. Ultimately, this approach presents a significant step forward in developing more robust and flexible SED models <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12721v2-abstract-full').style.display = 'none'; document.getElementById('2406.12721v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">DCASE 2024 challenge Task4, 4 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.11248">arXiv:2406.11248</a> <span> [<a href="https://arxiv.org/pdf/2406.11248">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Performance Improvement of Language-Queried Audio Source Separation Based on Caption Augmentation From Large Language Models for DCASE Challenge 2024 Task 9 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lee%2C+D+H">Do Hyun Lee</a>, <a href="/search/cs?searchtype=author&query=Song%2C+Y">Yoonah Song</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Hong Kook Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.11248v2-abstract-short" style="display: inline;"> We present a prompt-engineering-based text-augmentation approach applied to a language-queried audio source separation (LASS) task. To enhance the performance of LASS, the proposed approach utilizes large language models (LLMs) to generate multiple captions corresponding to each sentence of the training dataset. To this end, we first perform experiments to identify the most effective prompts for c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11248v2-abstract-full').style.display = 'inline'; document.getElementById('2406.11248v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.11248v2-abstract-full" style="display: none;"> We present a prompt-engineering-based text-augmentation approach applied to a language-queried audio source separation (LASS) task. To enhance the performance of LASS, the proposed approach utilizes large language models (LLMs) to generate multiple captions corresponding to each sentence of the training dataset. To this end, we first perform experiments to identify the most effective prompts for caption augmentation with a smaller number of captions. A LASS model trained with these augmented captions demonstrates improved performance on the DCASE 2024 Task 9 validation set compared to that trained without augmentation. This study highlights the effectiveness of LLM-based caption augmentation in advancing language-queried audio source separation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11248v2-abstract-full').style.display = 'none'; document.getElementById('2406.11248v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">DCASE 2024 Challenge Task 9, 4 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.00810">arXiv:2406.00810</a> <span> [<a href="https://arxiv.org/pdf/2406.00810">pdf</a>, <a href="https://arxiv.org/format/2406.00810">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Expanding the Attack Scenarios of SAE J1939: A Comprehensive Analysis of Established and Novel Vulnerabilities in Transport Protocol </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lee%2C+H">Hwejae Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+H">Hyosun Lee</a>, <a href="/search/cs?searchtype=author&query=Jun%2C+S">Saehee Jun</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.00810v1-abstract-short" style="display: inline;"> Following the enactment of the UN Regulation, substantial efforts have been directed toward implementing intrusion detection and prevention systems (IDPSs) and vulnerability analysis in Controller Area Network (CAN). However, Society of Automotive Engineers (SAE) J1939 protocol, despite its extensive application in camping cars and commercial vehicles, has seen limited vulnerability identification… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.00810v1-abstract-full').style.display = 'inline'; document.getElementById('2406.00810v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.00810v1-abstract-full" style="display: none;"> Following the enactment of the UN Regulation, substantial efforts have been directed toward implementing intrusion detection and prevention systems (IDPSs) and vulnerability analysis in Controller Area Network (CAN). However, Society of Automotive Engineers (SAE) J1939 protocol, despite its extensive application in camping cars and commercial vehicles, has seen limited vulnerability identification, which raises significant safety concerns in the event of security breaches. In this research, we explore and demonstrate attack techniques specific to SAE J1939 communication protocol. We introduce 14 attack scenarios, enhancing the discourse with seven scenarios recognized in the previous research and unveiling seven novel scenarios through our elaborate study. To verify the feasibility of these scenarios, we leverage a sophisticated testbed that facilitates real-time communication and the simulation of attacks. Our testing confirms the successful execution of 11 scenarios, underscoring their imminent threat to commercial vehicle operations. Some attacks will be difficult to detect because they only inject a single message. These results highlight unique vulnerabilities within SAE J1939 protocol, indicating the automotive cybersecurity community needs to address the identified risks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.00810v1-abstract-full').style.display = 'none'; document.getElementById('2406.00810v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 7 figures, 5 tables; This is the accepted version of ESCAR USA 2024</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68M25 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> K.6.5 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.15987">arXiv:2405.15987</a> <span> [<a href="https://arxiv.org/pdf/2405.15987">pdf</a>, <a href="https://arxiv.org/format/2405.15987">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Modes of Analyzing Disinformation Narratives With AI/ML/Text Mining to Assist in Mitigating the Weaponization of Social Media </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Skumanich%2C+A">Andy Skumanich</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Han Kyul Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.15987v1-abstract-short" style="display: inline;"> This paper highlights the developing need for quantitative modes for capturing and monitoring malicious communication in social media. There has been a deliberate "weaponization" of messaging through the use of social networks including by politically oriented entities both state sponsored and privately run. The article identifies a use of AI/ML characterization of generalized "mal-info," a broad… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.15987v1-abstract-full').style.display = 'inline'; document.getElementById('2405.15987v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.15987v1-abstract-full" style="display: none;"> This paper highlights the developing need for quantitative modes for capturing and monitoring malicious communication in social media. There has been a deliberate "weaponization" of messaging through the use of social networks including by politically oriented entities both state sponsored and privately run. The article identifies a use of AI/ML characterization of generalized "mal-info," a broad term which includes deliberate malicious narratives similar with hate speech, which adversely impact society. A key point of the discussion is that this mal-info will dramatically increase in volume, and it will become essential for sharable quantifying tools to provide support for human expert intervention. Despite attempts to introduce moderation on major platforms like Facebook and X/Twitter, there are now established alternative social networks that offer completely unmoderated spaces. The paper presents an introduction to these platforms and the initial results of a qualitative and semi-quantitative analysis of characteristic mal-info posts. The authors perform a rudimentary text mining function for a preliminary characterization in order to evaluate the modes for better-automated monitoring. The action examines several inflammatory terms using text analysis and, importantly, discusses the use of generative algorithms by one political agent in particular, providing some examples of the potential risks to society. This latter is of grave concern, and monitoring tools must be established. This paper presents a preliminary step to selecting relevant sources and to setting a foundation for characterizing the mal-info, which must be monitored. The AI/ML methods provide a means for semi-quantitative signature capture. The impending use of "mal-GenAI" is presented. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.15987v1-abstract-full').style.display = 'none'; document.getElementById('2405.15987v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at ICWSM-2024 Workshop on Digital State Sponsored Disinformation and Propaganda: Challenges and Opportunities (DSSDP24)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.04096">arXiv:2404.04096</a> <span> [<a href="https://arxiv.org/pdf/2404.04096">pdf</a>, <a href="https://arxiv.org/format/2404.04096">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Machine Learning-Aided Cooperative Localization under Dense Urban Environment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lee%2C+H">Hoon Lee</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Hong Ki Kim</a>, <a href="/search/cs?searchtype=author&query=Oh%2C+S+H">Seung Hyun Oh</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+S+H">Sang Hyun Lee</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.04096v1-abstract-short" style="display: inline;"> Future wireless network technology provides automobiles with the connectivity feature to consolidate the concept of vehicular networks that collaborate on conducting cooperative driving tasks. The full potential of connected vehicles, which promises road safety and quality driving experience, can be leveraged if machine learning models guarantee the robustness in performing core functions includin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04096v1-abstract-full').style.display = 'inline'; document.getElementById('2404.04096v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.04096v1-abstract-full" style="display: none;"> Future wireless network technology provides automobiles with the connectivity feature to consolidate the concept of vehicular networks that collaborate on conducting cooperative driving tasks. The full potential of connected vehicles, which promises road safety and quality driving experience, can be leveraged if machine learning models guarantee the robustness in performing core functions including localization and controls. Location awareness, in particular, lends itself to the deployment of location-specific services and the improvement of the operation performance. The localization entails direct communication to the network infrastructure, and the resulting centralized positioning solutions readily become intractable as the network scales up. As an alternative to the centralized solutions, this article addresses decentralized principle of vehicular localization reinforced by machine learning techniques in dense urban environments with frequent inaccessibility to reliable measurement. As such, the collaboration of multiple vehicles enhances the positioning performance of machine learning approaches. A virtual testbed is developed to validate this machine learning model for real-map vehicular networks. Numerical results demonstrate universal feasibility of cooperative localization, in particular, for dense urban area configurations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04096v1-abstract-full').style.display = 'none'; document.getElementById('2404.04096v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.07090">arXiv:2403.07090</a> <span> [<a href="https://arxiv.org/pdf/2403.07090">pdf</a>, <a href="https://arxiv.org/format/2403.07090">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Skumanich%2C+A">Andy Skumanich</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Han Kyul Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.07090v1-abstract-short" style="display: inline;"> Social media platforms hold valuable insights, yet extracting essential information can be challenging. Traditional top-down approaches often struggle to capture critical signals in rapidly changing events. As global events evolve swiftly, social media narratives, including instances of disinformation, become significant sources of insights. To address the need for an inductive strategy, we explor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07090v1-abstract-full').style.display = 'inline'; document.getElementById('2403.07090v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07090v1-abstract-full" style="display: none;"> Social media platforms hold valuable insights, yet extracting essential information can be challenging. Traditional top-down approaches often struggle to capture critical signals in rapidly changing events. As global events evolve swiftly, social media narratives, including instances of disinformation, become significant sources of insights. To address the need for an inductive strategy, we explore a niche social media platform GAB and an established messaging service Telegram, to develop methodologies applicable on a broader scale. This study investigates narrative evolution on these platforms using quantitative corpus-based discourse analysis techniques. Our approach is a novel mode to study multiple social media domains to distil key information which may be obscured otherwise, allowing for useful and actionable insights. The paper details the technical and methodological aspects of gathering and preprocessing GAB and Telegram data for a keyness (Log Ratio) metric analysis, identifying crucial nouns and verbs for deeper exploration. Empirically, this approach is applied to a case study of a well defined event that had global impact: the 2023 Wagner mutiny. The main findings are: (1) the time line can be deconstructed to provide useful data features allowing for improved interpretation; (2) a methodology is applied which provides a basis for generalization. The key contribution is an approach, that in some cases, provides the ability to capture the dynamic narrative shifts over time with elevated confidence. The approach can augment near-real-time assessment of key social movements, allowing for informed governance choices. This research is important because it lays out a useful methodology for time series relevant info-culling, which can enable proactive modes for positive social engagement. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07090v1-abstract-full').style.display = 'none'; document.getElementById('2403.07090v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">AAAI2024 Workshop on AI for Time Series Analysis (AI4TS)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.07082">arXiv:2403.07082</a> <span> [<a href="https://arxiv.org/pdf/2403.07082">pdf</a>, <a href="https://arxiv.org/ps/2403.07082">ps</a>, <a href="https://arxiv.org/format/2403.07082">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Exploring the Impact of ChatGPT on Student Interactions in Computer-Supported Collaborative Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Han Kyul Kim</a>, <a href="/search/cs?searchtype=author&query=Nayak%2C+S">Shriniwas Nayak</a>, <a href="/search/cs?searchtype=author&query=Roknaldin%2C+A">Aleyeh Roknaldin</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xiaoci Zhang</a>, <a href="/search/cs?searchtype=author&query=Twyman%2C+M">Marlon Twyman</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+S">Stephen Lu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.07082v1-abstract-short" style="display: inline;"> The growing popularity of generative AI, particularly ChatGPT, has sparked both enthusiasm and caution among practitioners and researchers in education. To effectively harness the full potential of ChatGPT in educational contexts, it is crucial to analyze its impact and suitability for different educational purposes. This paper takes an initial step in exploring the applicability of ChatGPT in a c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07082v1-abstract-full').style.display = 'inline'; document.getElementById('2403.07082v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07082v1-abstract-full" style="display: none;"> The growing popularity of generative AI, particularly ChatGPT, has sparked both enthusiasm and caution among practitioners and researchers in education. To effectively harness the full potential of ChatGPT in educational contexts, it is crucial to analyze its impact and suitability for different educational purposes. This paper takes an initial step in exploring the applicability of ChatGPT in a computer-supported collaborative learning (CSCL) environment. Using statistical analysis, we validate the shifts in student interactions during an asynchronous group brainstorming session by introducing ChatGPT as an instantaneous question-answering agent. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07082v1-abstract-full').style.display = 'none'; document.getElementById('2403.07082v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">AAAI2024 Workshop on AI for Education (AI4ED)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.14496">arXiv:2311.14496</a> <span> [<a href="https://arxiv.org/pdf/2311.14496">pdf</a>, <a href="https://arxiv.org/format/2311.14496">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> RTPS Attack Dataset Description </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+D+Y">Dong Young Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D">Dongsung Kim</a>, <a href="/search/cs?searchtype=author&query=Song%2C+Y">Yuchan Song</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+G+M">Gang Min Kim</a>, <a href="/search/cs?searchtype=author&query=Song%2C+M+G">Min Geun Song</a>, <a href="/search/cs?searchtype=author&query=Yoo%2C+J+D">Jeong Do Yoo</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.14496v4-abstract-short" style="display: inline;"> This paper explains all about our RTPS datasets. We collect malicious/benign packet data by injecting attack data in an Unmanned Ground Vehicle (UGV) in the normal state. We assembled the testbed, consisting of UGV, Controller, PC, and Router. We collect this dataset in the UGV part of our testbed. We conducted two types of attack "Command Injection" and "Command Injection with ARP Spoofing" on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14496v4-abstract-full').style.display = 'inline'; document.getElementById('2311.14496v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.14496v4-abstract-full" style="display: none;"> This paper explains all about our RTPS datasets. We collect malicious/benign packet data by injecting attack data in an Unmanned Ground Vehicle (UGV) in the normal state. We assembled the testbed, consisting of UGV, Controller, PC, and Router. We collect this dataset in the UGV part of our testbed. We conducted two types of attack "Command Injection" and "Command Injection with ARP Spoofing" on our testbed. The data collection time is 180, 300, 600, and 1200. The scenario has 30 each on collection time, 240 total. We expect this dataset to contribute to the development of defense technologies like anomaly detection to address security threat issues in ROS2 networks and Fast-DDS implements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14496v4-abstract-full').style.display = 'none'; document.getElementById('2311.14496v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This manuscript is written in Korean. You can download our dataset through our lab: https://ocslab.hksecurity.net/Datasets/rtps-attack-dataset We welcome your comments or feedback. Contact INFO: Dong Young Kim (klgh1256@korea.ac.kr), Huy Kang Kim (cenda@korea.ac.kr)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.14342">arXiv:2311.14342</a> <span> [<a href="https://arxiv.org/pdf/2311.14342">pdf</a>, <a href="https://arxiv.org/format/2311.14342">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> AI-based Attack Graph Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+S">Sangbeom Park</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+J">Jaesung Lee</a>, <a href="/search/cs?searchtype=author&query=Yoo%2C+J+D">Jeong Do Yoo</a>, <a href="/search/cs?searchtype=author&query=Song%2C+M+G">Min Geun Song</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+H">Hyosun Lee</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+J">Jaewoong Choi</a>, <a href="/search/cs?searchtype=author&query=Sagong%2C+C">Chaeyeon Sagong</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.14342v2-abstract-short" style="display: inline;"> With the advancement of IoT technology, many electronic devices are interconnected through networks, communicating with each other and performing specific roles. However, as numerous devices join networks, the threat of cyberattacks also escalates. Preventing and detecting cyber threats are crucial, and one method of preventing such threats involves using attack graphs. Attack graphs are widely us… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14342v2-abstract-full').style.display = 'inline'; document.getElementById('2311.14342v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.14342v2-abstract-full" style="display: none;"> With the advancement of IoT technology, many electronic devices are interconnected through networks, communicating with each other and performing specific roles. However, as numerous devices join networks, the threat of cyberattacks also escalates. Preventing and detecting cyber threats are crucial, and one method of preventing such threats involves using attack graphs. Attack graphs are widely used to assess security threats within networks. However, a drawback emerges as the network scales, as generating attack graphs becomes time-consuming. To overcome this limitation, artificial intelligence models can be employed. By utilizing AI models, attack graphs can be created within a short period, approximating optimal outcomes. AI models designed for attack graph generation consist of encoders and decoders, trained using reinforcement learning algorithms. After training the AI models, we confirmed the model's learning effectiveness by observing changes in loss and reward values. Additionally, we compared attack graphs generated by the AI model with those created through conventional methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14342v2-abstract-full').style.display = 'none'; document.getElementById('2311.14342v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">in Korean Language, 8 Figures, 14 Pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.14327">arXiv:2311.14327</a> <span> [<a href="https://arxiv.org/pdf/2311.14327">pdf</a>, <a href="https://arxiv.org/format/2311.14327">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> C-ITS Environment Modeling and Attack Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Choi%2C+J">Jaewoong Choi</a>, <a href="/search/cs?searchtype=author&query=Song%2C+M+G">Min Geun Song</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+H">Hyosun Lee</a>, <a href="/search/cs?searchtype=author&query=Sagong%2C+C">Chaeyeon Sagong</a>, <a href="/search/cs?searchtype=author&query=Park%2C+S">Sangbeom Park</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+J">Jaesung Lee</a>, <a href="/search/cs?searchtype=author&query=Yoo%2C+J+D">Jeong Do Yoo</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.14327v2-abstract-short" style="display: inline;"> As technology advances, cities are evolving into smart cities, with the ability to process large amounts of data and the increasing complexity and diversification of various elements within urban areas. Among the core systems of a smart city is the Cooperative-Intelligent Transport Systems (C-ITS). C-ITS is a system where vehicles provide real-time information to drivers about surrounding traffic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14327v2-abstract-full').style.display = 'inline'; document.getElementById('2311.14327v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.14327v2-abstract-full" style="display: none;"> As technology advances, cities are evolving into smart cities, with the ability to process large amounts of data and the increasing complexity and diversification of various elements within urban areas. Among the core systems of a smart city is the Cooperative-Intelligent Transport Systems (C-ITS). C-ITS is a system where vehicles provide real-time information to drivers about surrounding traffic conditions, sudden stops, falling objects, and other accident risks through roadside base stations. It consists of road infrastructure, C-ITS centers, and vehicle terminals. However, as smart cities integrate many elements through networks and electronic control, they are susceptible to cybersecurity issues. In the case of cybersecurity problems in C-ITS, there is a significant risk of safety issues arising. This technical document aims to model the C-ITS environment and the services it provides, with the purpose of identifying the attack surface where security incidents could occur in a smart city environment. Subsequently, based on the identified attack surface, the document aims to construct attack scenarios and their respective stages. The document provides a description of the concept of C-ITS, followed by the description of the C-ITS environment model, service model, and attack scenario model defined by us. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14327v2-abstract-full').style.display = 'none'; document.getElementById('2311.14327v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">in Korean Language, 14 Figures, 15 Pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.06461">arXiv:2306.06461</a> <span> [<a href="https://arxiv.org/pdf/2306.06461">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Semi-supervsied Learning-based Sound Event Detection using Freuqency Dynamic Convolution with Large Kernel Attention for DCASE Challenge 2023 Task 4 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+J+W">Ji Won Kim</a>, <a href="/search/cs?searchtype=author&query=Son%2C+S+W">Sang Won Son</a>, <a href="/search/cs?searchtype=author&query=Song%2C+Y">Yoonah Song</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Hong Kook Kim</a>, <a href="/search/cs?searchtype=author&query=Song%2C+I+H">Il Hoon Song</a>, <a href="/search/cs?searchtype=author&query=Lim%2C+J+E">Jeong Eun Lim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.06461v1-abstract-short" style="display: inline;"> This report proposes a frequency dynamic convolution (FDY) with a large kernel attention (LKA)-convolutional recurrent neural network (CRNN) with a pre-trained bidirectional encoder representation from audio transformers (BEATs) embedding-based sound event detection (SED) model that employs a mean-teacher and pseudo-label approach to address the challenge of limited labeled data for DCASE 2023 Tas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.06461v1-abstract-full').style.display = 'inline'; document.getElementById('2306.06461v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.06461v1-abstract-full" style="display: none;"> This report proposes a frequency dynamic convolution (FDY) with a large kernel attention (LKA)-convolutional recurrent neural network (CRNN) with a pre-trained bidirectional encoder representation from audio transformers (BEATs) embedding-based sound event detection (SED) model that employs a mean-teacher and pseudo-label approach to address the challenge of limited labeled data for DCASE 2023 Task 4. The proposed FDY with LKA integrates the FDY and LKA module to effectively capture time-frequency patterns, long-term dependencies, and high-level semantic information in audio signals. The proposed FDY with LKA-CRNN with a BEATs embedding network is initially trained on the entire DCASE 2023 Task 4 dataset using the mean-teacher approach, generating pseudo-labels for weakly labeled, unlabeled, and the AudioSet. Subsequently, the proposed SED model is retrained using the same pseudo-label approach. A subset of these models is selected for submission, demonstrating superior F1-scores and polyphonic SED score performance on the DCASE 2023 Challenge Task 4 validation dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.06461v1-abstract-full').style.display = 'none'; document.getElementById('2306.06461v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">DCASE 2023 Challenge Task 4A, 5 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.12278">arXiv:2303.12278</a> <span> [<a href="https://arxiv.org/pdf/2303.12278">pdf</a>, <a href="https://arxiv.org/format/2303.12278">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TVT.2023.3327275">10.1109/TVT.2023.3327275 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jeong%2C+S">Seonghoon Jeong</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+S">Sangho Lee</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+H">Hwejae Lee</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.12278v3-abstract-short" style="display: inline;"> Controller Area Network (CAN) is an essential networking protocol that connects multiple electronic control units (ECUs) in a vehicle. However, CAN-based in-vehicle networks (IVNs) face security risks owing to the CAN mechanisms. An adversary can sabotage a vehicle by leveraging the security risks if they can access the CAN bus. Thus, recent actions and cybersecurity regulations (e.g., UNR 155) re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.12278v3-abstract-full').style.display = 'inline'; document.getElementById('2303.12278v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.12278v3-abstract-full" style="display: none;"> Controller Area Network (CAN) is an essential networking protocol that connects multiple electronic control units (ECUs) in a vehicle. However, CAN-based in-vehicle networks (IVNs) face security risks owing to the CAN mechanisms. An adversary can sabotage a vehicle by leveraging the security risks if they can access the CAN bus. Thus, recent actions and cybersecurity regulations (e.g., UNR 155) require carmakers to implement intrusion detection systems (IDSs) in their vehicles. The IDS should detect cyberattacks and provide additional information to analyze conducted attacks. Although many IDSs have been proposed, considerations regarding their feasibility and explainability remain lacking. This study proposes X-CANIDS, which is a novel IDS for CAN-based IVNs. X-CANIDS dissects the payloads in CAN messages into human-understandable signals using a CAN database. The signals improve the intrusion detection performance compared with the use of bit representations of raw payloads. These signals also enable an understanding of which signal or ECU is under attack. X-CANIDS can detect zero-day attacks because it does not require any labeled dataset in the training phase. We confirmed the feasibility of the proposed method through a benchmark test on an automotive-grade embedded device with a GPU. The results of this work will be valuable to carmakers and researchers considering the installation of in-vehicle IDSs for their vehicles. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.12278v3-abstract-full').style.display = 'none'; document.getElementById('2303.12278v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This is the Accepted version of an article for publication in IEEE TVT</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Transactions on Vehicular Technology, Vol. 73, No. 3, pp. 3230-3246, Mar. 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.10854">arXiv:2212.10854</a> <span> [<a href="https://arxiv.org/pdf/2212.10854">pdf</a>, <a href="https://arxiv.org/format/2212.10854">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Defining C-ITS Environment and Attack Scenarios </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+Y">Yongsik Kim</a>, <a href="/search/cs?searchtype=author&query=Choi%2C+J+W">Jae Woong Choi</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+H+S">Hyo Sun Lee</a>, <a href="/search/cs?searchtype=author&query=Yoo%2C+J+D">Jeong Do Yoo</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H">Haerin Kim</a>, <a href="/search/cs?searchtype=author&query=Jang%2C+J">Junho Jang</a>, <a href="/search/cs?searchtype=author&query=Park%2C+K">Kibeom Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.10854v1-abstract-short" style="display: inline;"> As technology advances, it is possible to process a lot of data, and as various elements in the city become diverse and complex, cities are becoming smart cities. One of the core systems of smart cities is Cooperative-Intelligent Transport Systems (C-ITS). C-ITS is a system that provides drivers with real-time accident risk information such as surrounding traffic conditions, sudden stops, and fall… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.10854v1-abstract-full').style.display = 'inline'; document.getElementById('2212.10854v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.10854v1-abstract-full" style="display: none;"> As technology advances, it is possible to process a lot of data, and as various elements in the city become diverse and complex, cities are becoming smart cities. One of the core systems of smart cities is Cooperative-Intelligent Transport Systems (C-ITS). C-ITS is a system that provides drivers with real-time accident risk information such as surrounding traffic conditions, sudden stops, and falling objects while a vehicle is driving, and consists of road infrastructure, C-ITS center, and vehicle terminals. Meanwhile, smart cities can have cybersecurity problems because many elements of the city are networked and electronically controlled. If cybersecurity problems occur in C-ITS, there is a high risk of safety problems. The purpose of this technical document is to describe C-ITS environment modeling and C-ITS attack scenarios for C-ITS security. After describing the concept of C-ITS and MITRE ATT&CK, we describe the C-ITS environment model and the attack scenario model that we define. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.10854v1-abstract-full').style.display = 'none'; document.getElementById('2212.10854v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">in Korean language</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.09268">arXiv:2212.09268</a> <span> [<a href="https://arxiv.org/pdf/2212.09268">pdf</a>, <a href="https://arxiv.org/format/2212.09268">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> UAVCAN Dataset Description </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+D">Dongsung Kim</a>, <a href="/search/cs?searchtype=author&query=Song%2C+Y">Yuchan Song</a>, <a href="/search/cs?searchtype=author&query=Kwon%2C+S">Soonhyeon Kwon</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H">Haerin Kim</a>, <a href="/search/cs?searchtype=author&query=Yoo%2C+J+D">Jeong Do Yoo</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.09268v2-abstract-short" style="display: inline;"> We collected attack data from unmanned vehicles using the UAVCAN protocol, and public and described technical documents. A testbed was built with a drone using PX4, and a total of three attacks, Flooding, Fuzzy, and Replay, were performed. The attack was carried out in a total of 10 scenarios. We expect that the attack data will help develop technologies such as anomaly detection to solve the secu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.09268v2-abstract-full').style.display = 'inline'; document.getElementById('2212.09268v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.09268v2-abstract-full" style="display: none;"> We collected attack data from unmanned vehicles using the UAVCAN protocol, and public and described technical documents. A testbed was built with a drone using PX4, and a total of three attacks, Flooding, Fuzzy, and Replay, were performed. The attack was carried out in a total of 10 scenarios. We expect that the attack data will help develop technologies such as anomaly detection to solve the security threat problem of drones. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.09268v2-abstract-full').style.display = 'none'; document.getElementById('2212.09268v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">in english language</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.10297">arXiv:2207.10297</a> <span> [<a href="https://arxiv.org/pdf/2207.10297">pdf</a>, <a href="https://arxiv.org/format/2207.10297">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Action2Score: An Embedding Approach To Score Player Action </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jang%2C+J">Junho Jang</a>, <a href="/search/cs?searchtype=author&query=Woo%2C+J+Y">Ji Young Woo</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.10297v1-abstract-short" style="display: inline;"> Multiplayer Online Battle Arena (MOBA) is one of the most successful game genres. MOBA games such as League of Legends have competitive environments where players race for their rank. In most MOBA games, a player's rank is determined by the match result (win or lose). It seems natural because of the nature of team play, but in some sense, it is unfair because the players who put a lot of effort lo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.10297v1-abstract-full').style.display = 'inline'; document.getElementById('2207.10297v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.10297v1-abstract-full" style="display: none;"> Multiplayer Online Battle Arena (MOBA) is one of the most successful game genres. MOBA games such as League of Legends have competitive environments where players race for their rank. In most MOBA games, a player's rank is determined by the match result (win or lose). It seems natural because of the nature of team play, but in some sense, it is unfair because the players who put a lot of effort lose their rank just in case of loss and some players even get free-ride on teammates' efforts in case of a win. To reduce the side-effects of the team-based ranking system and evaluate a player's performance impartially, we propose a novel embedding model that converts a player's actions into quantitative scores based on the actions' respective contribution to the team's victory. Our model is built using a sequence-based deep learning model with a novel loss function working on the team match. The sequence-based deep learning model process the action sequence from the game start to the end of a player in a team play using a GRU unit that takes a hidden state from the previous step and the current input selectively. The loss function is designed to help the action score to reflect the final score and the success of the team. We showed that our model can evaluate a player's individual performance fairly and analyze the contributions of the player's respective actions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.10297v1-abstract-full').style.display = 'none'; document.getElementById('2207.10297v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 8 figures, 4 tables; accepted to ACM CHIPLAY 2022, and PACM on Human-Computer Interaction</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.06113">arXiv:2204.06113</a> <span> [<a href="https://arxiv.org/pdf/2204.06113">pdf</a>, <a href="https://arxiv.org/format/2204.06113">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Liuer Mihou: A Practical Framework for Generating and Evaluating Grey-box Adversarial Attacks against NIDS </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=He%2C+K">Ke He</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+D">Dan Dongseong Kim</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+J">Jing Sun</a>, <a href="/search/cs?searchtype=author&query=Yoo%2C+J+D">Jeong Do Yoo</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+Y+H">Young Hun Lee</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.06113v1-abstract-short" style="display: inline;"> Due to its high expressiveness and speed, Deep Learning (DL) has become an increasingly popular choice as the detection algorithm for Network-based Intrusion Detection Systems (NIDSes). Unfortunately, DL algorithms are vulnerable to adversarial examples that inject imperceptible modifications to the input and cause the DL algorithm to misclassify the input. Existing adversarial attacks in the NIDS… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.06113v1-abstract-full').style.display = 'inline'; document.getElementById('2204.06113v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.06113v1-abstract-full" style="display: none;"> Due to its high expressiveness and speed, Deep Learning (DL) has become an increasingly popular choice as the detection algorithm for Network-based Intrusion Detection Systems (NIDSes). Unfortunately, DL algorithms are vulnerable to adversarial examples that inject imperceptible modifications to the input and cause the DL algorithm to misclassify the input. Existing adversarial attacks in the NIDS domain often manipulate the traffic features directly, which hold no practical significance because traffic features cannot be replayed in a real network. It remains a research challenge to generate practical and evasive adversarial attacks. This paper presents the Liuer Mihou attack that generates practical and replayable adversarial network packets that can bypass anomaly-based NIDS deployed in the Internet of Things (IoT) networks. The core idea behind Liuer Mihou is to exploit adversarial transferability and generate adversarial packets on a surrogate NIDS constrained by predefined mutation operations to ensure practicality. We objectively analyse the evasiveness of Liuer Mihou against four ML-based algorithms (LOF, OCSVM, RRCF, and SOM) and the state-of-the-art NIDS, Kitsune. From the results of our experiment, we gain valuable insights into necessary conditions on the adversarial transferability of anomaly detection algorithms. Going beyond a theoretical setting, we replay the adversarial attack in a real IoT testbed to examine the practicality of Liuer Mihou. Furthermore, we demonstrate that existing feature-level adversarial defence cannot defend against Liuer Mihou and constructively criticise the limitations of feature-level adversarial defences. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.06113v1-abstract-full').style.display = 'none'; document.getElementById('2204.06113v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 8 figures, planning on submitting to ACM CCS 2022</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.13595">arXiv:2112.13595</a> <span> [<a href="https://arxiv.org/pdf/2112.13595">pdf</a>, <a href="https://arxiv.org/format/2112.13595">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Depth estimation of endoscopy using sim-to-real transfer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jeong%2C+B+H">Bong Hyuk Jeong</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Hang Keun Kim</a>, <a href="/search/cs?searchtype=author&query=Son%2C+Y+D">Young Don Son</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.13595v1-abstract-short" style="display: inline;"> In order to use the navigation system effectively, distance information sensors such as depth sensors are essential. Since depth sensors are difficult to use in endoscopy, many groups propose a method using convolutional neural networks. In this paper, the ground truth of the depth image and the endoscopy image is generated through endoscopy simulation using the colon model segmented by CT colonog… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.13595v1-abstract-full').style.display = 'inline'; document.getElementById('2112.13595v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.13595v1-abstract-full" style="display: none;"> In order to use the navigation system effectively, distance information sensors such as depth sensors are essential. Since depth sensors are difficult to use in endoscopy, many groups propose a method using convolutional neural networks. In this paper, the ground truth of the depth image and the endoscopy image is generated through endoscopy simulation using the colon model segmented by CT colonography. Photo-realistic simulation images can be created using a sim-to-real approach using cycleGAN for endoscopy images. By training the generated dataset, we propose a quantitative endoscopy depth estimation network. The proposed method represents a better-evaluated score than the existing unsupervised training-based results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.13595v1-abstract-full').style.display = 'none'; document.getElementById('2112.13595v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 9 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T05; 62P10 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.07116">arXiv:2110.07116</a> <span> [<a href="https://arxiv.org/pdf/2110.07116">pdf</a>, <a href="https://arxiv.org/format/2110.07116">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Auxiliary Loss of Transformer with Residual Connection for End-to-End Speaker Diarization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yu%2C+Y">Yechan Yu</a>, <a href="/search/cs?searchtype=author&query=Park%2C+D">Dongkeon Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Hong Kook Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.07116v3-abstract-short" style="display: inline;"> End-to-end neural diarization (EEND) with self-attention directly predicts speaker labels from inputs and enables the handling of overlapped speech. Although the EEND outperforms clustering-based speaker diarization (SD), it cannot be further improved by simply increasing the number of encoder blocks because the last encoder block is dominantly supervised compared with lower blocks. This paper pro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.07116v3-abstract-full').style.display = 'inline'; document.getElementById('2110.07116v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.07116v3-abstract-full" style="display: none;"> End-to-end neural diarization (EEND) with self-attention directly predicts speaker labels from inputs and enables the handling of overlapped speech. Although the EEND outperforms clustering-based speaker diarization (SD), it cannot be further improved by simply increasing the number of encoder blocks because the last encoder block is dominantly supervised compared with lower blocks. This paper proposes a new residual auxiliary EEND (RX-EEND) learning architecture for transformers to enforce the lower encoder blocks to learn more accurately. The auxiliary loss is applied to the output of each encoder block, including the last encoder block. The effect of auxiliary loss on the learning of the encoder blocks can be further increased by adding a residual connection between the encoder blocks of the EEND. Performance evaluation and ablation study reveal that the auxiliary loss in the proposed RX-EEND provides relative reductions in the diarization error rate (DER) by 50.3% and 21.0% on the simulated and CALLHOME (CH) datasets, respectively, compared with self-attentive EEND (SA-EEND). Furthermore, the residual connection used in RX-EEND further relatively reduces the DER by 8.1% for CH dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.07116v3-abstract-full').style.display = 'none'; document.getElementById('2110.07116v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to ICASSP 2022, equal contribution from first two authors</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.05079">arXiv:2108.05079</a> <span> [<a href="https://arxiv.org/pdf/2108.05079">pdf</a>, <a href="https://arxiv.org/format/2108.05079">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Unsupervised Driver Behavior Profiling leveraging Recurrent Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Choi%2C+Y+A">Young Ah Choi</a>, <a href="/search/cs?searchtype=author&query=Park%2C+K+H">Kyung Ho Park</a>, <a href="/search/cs?searchtype=author&query=Park%2C+E">Eunji Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.05079v1-abstract-short" style="display: inline;"> In the era of intelligent transportation, driver behavior profiling has become a beneficial technology as it provides knowledge regarding the driver's aggressiveness. Previous approaches achieved promising driver behavior profiling performance through establishing statistical heuristics rules or supervised learning-based models. Still, there exist limits that the practitioner should prepare a labe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.05079v1-abstract-full').style.display = 'inline'; document.getElementById('2108.05079v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.05079v1-abstract-full" style="display: none;"> In the era of intelligent transportation, driver behavior profiling has become a beneficial technology as it provides knowledge regarding the driver's aggressiveness. Previous approaches achieved promising driver behavior profiling performance through establishing statistical heuristics rules or supervised learning-based models. Still, there exist limits that the practitioner should prepare a labeled dataset, and prior approaches could not classify aggressive behaviors which are not known a priori. In pursuit of improving the aforementioned drawbacks, we propose a novel approach to driver behavior profiling leveraging an unsupervised learning paradigm. First, we cast the driver behavior profiling problem as anomaly detection. Second, we established recurrent neural networks that predict the next feature vector given a sequence of feature vectors. We trained the model with normal driver data only. As a result, our model yields high regression error given a sequence of aggressive driver behavior and low error given at a sequence of normal driver behavior. We figured this difference of error between normal and aggressive driver behavior can be an adequate flag for driver behavior profiling and accomplished a precise performance in experiments. Lastly, we further analyzed the optimal level of sequence length for identifying each aggressive driver behavior. We expect the proposed approach to be a useful baseline for unsupervised driver behavior profiling and contribute to the efficient, intelligent transportation ecosystem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.05079v1-abstract-full').style.display = 'none'; document.getElementById('2108.05079v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 2 figures, 1 table, this paper is accepted in WISA 2021</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2; I.5.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.02569">arXiv:2107.02569</a> <span> [<a href="https://arxiv.org/pdf/2107.02569">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Self-training with noisy student model and semi-supervised loss function for dcase 2021 challenge task 4 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+N+K">Nam Kyun Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Hong Kook Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.02569v1-abstract-short" style="display: inline;"> This report proposes a polyphonic sound event detection (SED) method for the DCASE 2021 Challenge Task 4. The proposed SED model consists of two stages: a mean-teacher model for providing target labels regarding weakly labeled or unlabeled data and a self-training-based noisy student model for predicting strong labels for sound events. The mean-teacher model, which is based on the residual convolu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.02569v1-abstract-full').style.display = 'inline'; document.getElementById('2107.02569v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.02569v1-abstract-full" style="display: none;"> This report proposes a polyphonic sound event detection (SED) method for the DCASE 2021 Challenge Task 4. The proposed SED model consists of two stages: a mean-teacher model for providing target labels regarding weakly labeled or unlabeled data and a self-training-based noisy student model for predicting strong labels for sound events. The mean-teacher model, which is based on the residual convolutional recurrent neural network (RCRNN) for the teacher and student model, is first trained using all the training data from a weakly labeled dataset, an unlabeled dataset, and a strongly labeled synthetic dataset. Then, the trained mean-teacher model predicts the strong label to each of the weakly labeled and unlabeled datasets, which is brought to the noisy student model in the second stage of the proposed SED model. Here, the structure of the noisy student model is identical to the RCRNN-based student model of the mean-teacher model in the first stage. Then, it is self-trained by adding feature noises, such as time-frequency shift, mixup, SpecAugment, and dropout-based model noise. In addition, a semi-supervised loss function is applied to train the noisy student model, which acts as label noise injection. The performance of the proposed SED model is evaluated on the validation set of the DCASE 2021 Challenge Task 4, and then, several ensemble models that combine five-fold validation models with different hyperparameters of the semi-supervised loss function are finally selected as our final models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.02569v1-abstract-full').style.display = 'none'; document.getElementById('2107.02569v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, DCASE 2021 challenge Task 4 technical report</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.03546">arXiv:2102.03546</a> <span> [<a href="https://arxiv.org/pdf/2102.03546">pdf</a>, <a href="https://arxiv.org/format/2102.03546">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jeong%2C+S">Seonghoon Jeong</a>, <a href="/search/cs?searchtype=author&query=Jeon%2C+B">Boosun Jeon</a>, <a href="/search/cs?searchtype=author&query=Chung%2C+B">Boheung Chung</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.03546v1-abstract-short" style="display: inline;"> Connected and autonomous vehicles (CAVs) are an innovative form of traditional vehicles. Automotive Ethernet replaces the controller area network and FlexRay to support the large throughput required by high-definition applications. As CAVs have numerous functions, they exhibit a large attack surface and an increased vulnerability to attacks. However, no previous studies have focused on intrusion d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.03546v1-abstract-full').style.display = 'inline'; document.getElementById('2102.03546v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.03546v1-abstract-full" style="display: none;"> Connected and autonomous vehicles (CAVs) are an innovative form of traditional vehicles. Automotive Ethernet replaces the controller area network and FlexRay to support the large throughput required by high-definition applications. As CAVs have numerous functions, they exhibit a large attack surface and an increased vulnerability to attacks. However, no previous studies have focused on intrusion detection in automotive Ethernet-based networks. In this paper, we present an intrusion detection method for detecting audio-video transport protocol (AVTP) stream injection attacks in automotive Ethernet-based networks. To the best of our knowledge, this is the first such method developed for automotive Ethernet. The proposed intrusion detection model is based on feature generation and a convolutional neural network (CNN). To evaluate our intrusion detection system, we built a physical BroadR-Reach-based testbed and captured real AVTP packets. The experimental results show that the model exhibits outstanding performance: the F1-score and recall are greater than 0.9704 and 0.9949, respectively. In terms of the inference time per input and the generation intervals of AVTP traffic, our CNN model can readily be employed for real-time detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.03546v1-abstract-full').style.display = 'none'; document.getElementById('2102.03546v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">35 pages, 9 figures, accepted to Vehicular Communications (Elsevier)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> C.2.5 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.13374">arXiv:2011.13374</a> <span> [<a href="https://arxiv.org/pdf/2011.13374">pdf</a>, <a href="https://arxiv.org/format/2011.13374">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Understand Watchdogs: Discover How Game Bot Get Discovered </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+E">Eunji Park</a>, <a href="/search/cs?searchtype=author&query=Park%2C+K+H">Kyung Ho Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.13374v2-abstract-short" style="display: inline;"> The game industry has long been troubled by malicious activities utilizing game bots. The game bots disturb other game players and destroy the environmental system of the games. For these reasons, the game industry put their best efforts to detect the game bots among players' characters using the learning-based detections. However, one problem with the detection methodologies is that they do not p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.13374v2-abstract-full').style.display = 'inline'; document.getElementById('2011.13374v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.13374v2-abstract-full" style="display: none;"> The game industry has long been troubled by malicious activities utilizing game bots. The game bots disturb other game players and destroy the environmental system of the games. For these reasons, the game industry put their best efforts to detect the game bots among players' characters using the learning-based detections. However, one problem with the detection methodologies is that they do not provide rational explanations about their decisions. To resolve this problem, in this work, we investigate the explainabilities of the game bot detection. We develop the XAI model using a dataset from the Korean MMORPG, AION, which includes game logs of human players and game bots. More than one classification model has been applied to the dataset to be analyzed by applying interpretable models. This provides us explanations about the game bots' behavior, and the truthfulness of the explanations has been evaluated. Besides, interpretability contributes to minimizing false detection, which imposes unfair restrictions on human players. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.13374v2-abstract-full').style.display = 'none'; document.getElementById('2011.13374v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 3 figures, 3 tables, this paper is accepted in ICAART 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.00540">arXiv:2011.00540</a> <span> [<a href="https://arxiv.org/pdf/2011.00540">pdf</a>, <a href="https://arxiv.org/format/2011.00540">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Unsupervised Intrusion Detection System for Unmanned Aerial Vehicle with Less Labeling Effort </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+K+H">Kyung Ho Park</a>, <a href="/search/cs?searchtype=author&query=Park%2C+E">Eunji Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.00540v1-abstract-short" style="display: inline;"> Along with the importance of safety, an IDS has become a significant task in the real world. Prior studies proposed various intrusion detection models for the UAV. Past rule-based approaches provided a concrete baseline IDS model, and the machine learning-based method achieved a precise intrusion detection performance on the UAV with supervised learning models. However, previous methods have room… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.00540v1-abstract-full').style.display = 'inline'; document.getElementById('2011.00540v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.00540v1-abstract-full" style="display: none;"> Along with the importance of safety, an IDS has become a significant task in the real world. Prior studies proposed various intrusion detection models for the UAV. Past rule-based approaches provided a concrete baseline IDS model, and the machine learning-based method achieved a precise intrusion detection performance on the UAV with supervised learning models. However, previous methods have room for improvement to be implemented in the real world. Prior methods required a large labeling effort on the dataset, and the model could not identify attacks that were not trained before. To jump over these hurdles, we propose an IDS with unsupervised learning. As unsupervised learning does not require labeling, our model let the practitioner not to label every type of attack from the flight data. Moreover, the model can identify an abnormal status of the UAV regardless of the type of attack. We trained an autoencoder with the benign flight data only and checked the model provides a different reconstruction loss at the benign flight and the flight under attack. We discovered that the model produces much higher reconstruction loss with the flight under attack than the benign flight; thus, this reconstruction loss can be utilized to recognize an intrusion to the UAV. With consideration of the computation overhead and the detection performance in the wild, we expect our model can be a concrete and practical baseline IDS on the UAV. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.00540v1-abstract-full').style.display = 'none'; document.getElementById('2011.00540v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 4 tables, 3 figures, 5 equations, In Proceeding of WISA 2020 (THE 21ST WORLD CONFERENCE ON INFORMATION SECURITY APPLICATIONS)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.12116">arXiv:2009.12116</a> <span> [<a href="https://arxiv.org/pdf/2009.12116">pdf</a>, <a href="https://arxiv.org/format/2009.12116">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1155/2020/8869418">10.1155/2020/8869418 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Beyond PS-LTE: Security Model Design Framework for PPDR Operational Environment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+D">Daegeon Kim</a>, <a href="/search/cs?searchtype=author&query=Gu%2C+D+H">Do Hyung Gu</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.12116v3-abstract-short" style="display: inline;"> National disasters can threaten national security and require several organizations to integrate the functionalities to correspond to the event. Many countries are constructing a nationwide mobile communication network infrastructure to share information and promptly communicate with corresponding organizations. Public Safety Long-Term Evolution (PS-LTE) is a communication mechanism adopted in man… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.12116v3-abstract-full').style.display = 'inline'; document.getElementById('2009.12116v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.12116v3-abstract-full" style="display: none;"> National disasters can threaten national security and require several organizations to integrate the functionalities to correspond to the event. Many countries are constructing a nationwide mobile communication network infrastructure to share information and promptly communicate with corresponding organizations. Public Safety Long-Term Evolution (PS-LTE) is a communication mechanism adopted in many countries to achieve such a purpose. Organizations can increase the efficiency of public protection and disaster relief (PPDR) operations by securely connecting the services run on their legacy networks to the PS-LTE infrastructure. This environment allows the organizations to continue facilitating the information and system functionalities provided by the legacy network. The vulnerabilities in the environment, which differ from commercial LTE, need to be resolved to connect the network securely. In this study, we propose a security model design framework to derive the system architecture and the security requirements targeting the restricted environment applied by certain technologies for a particular purpose. After analyzing the PPDR operation environment's characteristics under the PS-LTE infrastructure, we applied the framework to derive the security model for organizations using PPDR services operated in their legacy networks through this infrastructure. Although the proposed security model design framework is applied to the specific circumstance in this research, it can be generally adopted for the application environment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.12116v3-abstract-full').style.display = 'none'; document.getElementById('2009.12116v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Security and Communication Networks, vol. 2020, Article ID 8869418 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.07212">arXiv:2008.07212</a> <span> [<a href="https://arxiv.org/pdf/2008.07212">pdf</a>, <a href="https://arxiv.org/ps/2008.07212">ps</a>, <a href="https://arxiv.org/format/2008.07212">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Combinatorics">math.CO</span> </div> </div> <p class="title is-5 mathjax"> Optimal minimal Linear codes from posets </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hyun%2C+J+Y">Jong Yoon Hyun</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Hyun Kwang Kim</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Yansheng Wu</a>, <a href="/search/cs?searchtype=author&query=Yue%2C+Q">Qin Yue</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2008.07212v1-abstract-short" style="display: inline;"> Recently, some infinite families of minimal and optimal binary linear codes were constructed from simplicial complexes by Hyun {\em et al.} We extend this construction method to arbitrary posets. Especially, anti-chains are corresponded to simplicial complexes. In this paper, we present two constructions of binary linear codes from hierarchical posets of two levels. In particular, we determine t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.07212v1-abstract-full').style.display = 'inline'; document.getElementById('2008.07212v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.07212v1-abstract-full" style="display: none;"> Recently, some infinite families of minimal and optimal binary linear codes were constructed from simplicial complexes by Hyun {\em et al.} We extend this construction method to arbitrary posets. Especially, anti-chains are corresponded to simplicial complexes. In this paper, we present two constructions of binary linear codes from hierarchical posets of two levels. In particular, we determine the weight distributions of binary linear codes associated with hierarchical posets with two levels. Based on these results, we also obtain some optimal and minimal binary linear codes not satisfying the condition of Ashikhmin-Barg. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.07212v1-abstract-full').style.display = 'none'; document.getElementById('2008.07212v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, to appear in DCC</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.00947">arXiv:2007.00947</a> <span> [<a href="https://arxiv.org/pdf/2007.00947">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Polyphonic sound event detection based on convolutional recurrent neural networks with semi-supervised loss function for DCASE challenge 2020 task 4 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+N+K">Nam Kyun Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Hong Kook Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.00947v1-abstract-short" style="display: inline;"> This report proposes a polyphonic sound event detection (SED) method for the DCASE 2020 Challenge Task 4. The proposed SED method is based on semi-supervised learning to deal with the different combination of training datasets such as weakly labeled dataset, unlabeled dataset, and strongly labeled synthetic dataset. Especially, the target label of each audio clip from weakly labeled or unlabeled d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.00947v1-abstract-full').style.display = 'inline'; document.getElementById('2007.00947v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.00947v1-abstract-full" style="display: none;"> This report proposes a polyphonic sound event detection (SED) method for the DCASE 2020 Challenge Task 4. The proposed SED method is based on semi-supervised learning to deal with the different combination of training datasets such as weakly labeled dataset, unlabeled dataset, and strongly labeled synthetic dataset. Especially, the target label of each audio clip from weakly labeled or unlabeled dataset is first predicted by using the mean teacher model that is the DCASE 2020 baseline. The data with predicted labels are used for training the proposed SED model, which consists of CNNs with skip connections and self-attention mechanism, followed by RNNs. In order to compensate for the erroneous prediction of weakly labeled and unlabeled data, a semi-supervised loss function is employed for the proposed SED model. In this work, several versions of the proposed SED model are implemented and evaluated on the validation set according to the different parameter setting for the semi-supervised loss function, and then an ensemble model that combines five-fold validation models is finally selected as our final model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.00947v1-abstract-full').style.display = 'none'; document.getElementById('2007.00947v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">4 pages, DCASE challenge 2020 task4 technical report</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.09870">arXiv:1911.09870</a> <span> [<a href="https://arxiv.org/pdf/1911.09870">pdf</a>, <a href="https://arxiv.org/format/1911.09870">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+K+H">Kyung Ho Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.09870v1-abstract-short" style="display: inline;"> As a car becomes more connected, a countermeasure against automobile theft has become a significant task in the real world. To respond to automobile theft, data mining, biometrics, and additional authentication methods are proposed. Among current countermeasures, data mining method is one of the efficient ways to capture the owner driver's unique characteristics. To identify the owner driver from… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.09870v1-abstract-full').style.display = 'inline'; document.getElementById('1911.09870v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.09870v1-abstract-full" style="display: none;"> As a car becomes more connected, a countermeasure against automobile theft has become a significant task in the real world. To respond to automobile theft, data mining, biometrics, and additional authentication methods are proposed. Among current countermeasures, data mining method is one of the efficient ways to capture the owner driver's unique characteristics. To identify the owner driver from thieves, previous works applied various algorithms toward driving data. Such data mining methods utilized supervised learning, thus required labeled data set. However, it is unrealistic to gather and apply the thief's driving pattern. To overcome this problem, we propose driver identification method with GAN. GAN has merit to build identification model by learning the owner driver's data only. We trained GAN only with owner driver's data and used trained discriminator to identify the owner driver. From actual driving data, we evaluated our identification model recognizes the owner driver well. By ensembling various driver authentication methods with the proposed model, we expect industry can develop automobile theft countermeasures available in the real world. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.09870v1-abstract-full').style.display = 'none'; document.getElementById('1911.09870v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 3 figures, 3 tables, In Proceedings of the escar Asia 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.10851">arXiv:1909.10851</a> <span> [<a href="https://arxiv.org/pdf/1909.10851">pdf</a>, <a href="https://arxiv.org/format/1909.10851">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Oldie is Goodie: Effective User Retention by In-game Promotion Event Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+K+H">Kyoung Ho Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1909.10851v1-abstract-short" style="display: inline;"> For sustainable growth and profitability, online game companies are constantly carrying out various events to attract new game users, to maximize return users, and to minimize churn users in online games. Because minimizing churn users is the most cost-effective method, many pieces of research are being conducted on ways to predict and to prevent churns in advance. However, there is still little r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.10851v1-abstract-full').style.display = 'inline'; document.getElementById('1909.10851v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.10851v1-abstract-full" style="display: none;"> For sustainable growth and profitability, online game companies are constantly carrying out various events to attract new game users, to maximize return users, and to minimize churn users in online games. Because minimizing churn users is the most cost-effective method, many pieces of research are being conducted on ways to predict and to prevent churns in advance. However, there is still little research on the validity of event effects. In this study, we investigate whether game events influence the user churn rate and confirm the difference in how game users respond to events by character level, item purchasing frequency and game-playing time band. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.10851v1-abstract-full').style.display = 'none'; document.getElementById('1909.10851v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 7 figures, 5 tables; This paper is an author version of the same title paper appeared in the proceedings on ACM CHIPLAY 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.08929">arXiv:1909.08929</a> <span> [<a href="https://arxiv.org/pdf/1909.08929">pdf</a>, <a href="https://arxiv.org/format/1909.08929">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.13154/294-6675">10.13154/294-6675 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Automobile Theft Detection by Clustering Owner Driver Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+Y+G">Yong Goo Kang</a>, <a href="/search/cs?searchtype=author&query=Park%2C+K+H">Kyung Ho Park</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1909.08929v1-abstract-short" style="display: inline;"> As automobiles become intelligent, automobile theft methods are evolving intelligently. Therefore automobile theft detection has become a major research challenge. Data-mining, biometrics, and additional authentication methods have been proposed to address automobile theft, in previous studies. Among these methods, data-mining can be used to analyze driving characteristics and identify a driver co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.08929v1-abstract-full').style.display = 'inline'; document.getElementById('1909.08929v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.08929v1-abstract-full" style="display: none;"> As automobiles become intelligent, automobile theft methods are evolving intelligently. Therefore automobile theft detection has become a major research challenge. Data-mining, biometrics, and additional authentication methods have been proposed to address automobile theft, in previous studies. Among these methods, data-mining can be used to analyze driving characteristics and identify a driver comprehensively. However, it requires a labeled driving dataset to achieve high accuracy. It is impractical to use the actual automobile theft detection system because real theft driving data cannot be collected in advance. Hence, we propose a method to detect an automobile theft attempt using only owner driving data. We cluster the key features of the owner driving data using the k-means algorithm. After reconstructing the driving data into one of these clusters, theft is detected using an error from the original driving data. To validate the proposed models, we tested our actual driving data and obtained 99% accuracy from the best model. This result demonstrates that our proposed method can detect vehicle theft by using only the car owner's driving data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.08929v1-abstract-full').style.display = 'none'; document.getElementById('1909.08929v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 7 figures, 3 tables, In Proceedings of the 17th escar Europe 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1909.02786">arXiv:1909.02786</a> <span> [<a href="https://arxiv.org/pdf/1909.02786">pdf</a>, <a href="https://arxiv.org/format/1909.02786">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Security Requirements of Commercial Drones for Public Authorities by Vulnerability Analysis of Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+D">Daegeon Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1909.02786v1-abstract-short" style="display: inline;"> Due to the ability to overcome the geospatial limitations and to the possibility to converge the various information communication technologies, the application domains and the market size of drones are increasing internationally. Public authorities in South Korean are investing for the domestic drone industry and the technological advancement as a power of innovation and growth of the country. Th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.02786v1-abstract-full').style.display = 'inline'; document.getElementById('1909.02786v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1909.02786v1-abstract-full" style="display: none;"> Due to the ability to overcome the geospatial limitations and to the possibility to converge the various information communication technologies, the application domains and the market size of drones are increasing internationally. Public authorities in South Korean are investing for the domestic drone industry and the technological advancement as a power of innovation and growth of the country. They are also increasing the utilization of drones for various purposes. The South Korean government ensures the security of IT equipment introduced to the public authorities by enforcing policies such as security compatibility verification and CCTV security certification. Considering the increase of the needs of drones and the possible security effects to the organization operating them, the government needs to develop the security requirements during introducing drones, but there are no such requirements yet. In this paper, we inspect the vulnerabilities of drones by analyzing the applications of commercial drones made by 4 manufacturers. We also propose the minimum security requirements to resolve the vulnerabilities. We expect our work contributes to the security improvements of drones operated in public authorities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1909.02786v1-abstract-full').style.display = 'none'; document.getElementById('1909.02786v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 12 figures, 5 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1908.03748">arXiv:1908.03748</a> <span> [<a href="https://arxiv.org/pdf/1908.03748">pdf</a>, <a href="https://arxiv.org/format/1908.03748">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Show Me Your Account: Detecting MMORPG Game Bot Leveraging Financial Analysis with LSTM </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+K+H">Kyung Ho Park</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+E">Eunjo Lee</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1908.03748v1-abstract-short" style="display: inline;"> With the rapid growth of MMORPG market, game bot detection has become an essential task for maintaining stable in-game ecosystem. To classify bots from normal users, detection methods are proposed in both game client and server-side. Among various classification methods, data mining method in server-side captured unique characteristics of bots efficiently. For features used in data mining, behavio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.03748v1-abstract-full').style.display = 'inline'; document.getElementById('1908.03748v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.03748v1-abstract-full" style="display: none;"> With the rapid growth of MMORPG market, game bot detection has become an essential task for maintaining stable in-game ecosystem. To classify bots from normal users, detection methods are proposed in both game client and server-side. Among various classification methods, data mining method in server-side captured unique characteristics of bots efficiently. For features used in data mining, behavioral and social actions of character are analyzed with numerous algorithms. However, bot developers can evade the previous detection methods by changing bot's activities continuously. Eventually, overall maintenance cost increases because the selected features need to be updated along with the change of bot's behavior. To overcome this limitation, we propose improved bot detection method with financial analysis. As bot's activity absolutely necessitates the change of financial status, analyzing financial fluctuation effectively captures bots as a key feature. We trained and tested model with actual data of Aion, a leading MMORPG in Asia. Leveraging that LSTM efficiently recognizes time-series movement of data, we achieved meaningful detection performance. Further on this model, we expect sustainable bot detection system in the near future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.03748v1-abstract-full').style.display = 'none'; document.getElementById('1908.03748v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 1 figure, 5 tables, In Proceedings of the 20th World Conference on Information Security and Applications (WISA) 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1907.07377">arXiv:1907.07377</a> <span> [<a href="https://arxiv.org/pdf/1907.07377">pdf</a>, <a href="https://arxiv.org/format/1907.07377">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/PST.2018.8514157">10.1109/PST.2018.8514157 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> GIDS: GAN based Intrusion Detection System for In-Vehicle Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Seo%2C+E">Eunbi Seo</a>, <a href="/search/cs?searchtype=author&query=Song%2C+H+M">Hyun Min Song</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1907.07377v1-abstract-short" style="display: inline;"> A Controller Area Network (CAN) bus in the vehicles is an efficient standard bus enabling communication between all Electronic Control Units (ECU). However, CAN bus is not enough to protect itself because of lack of security features. To detect suspicious network connections effectively, the intrusion detection system (IDS) is strongly required. Unlike the traditional IDS for Internet, there are s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.07377v1-abstract-full').style.display = 'inline'; document.getElementById('1907.07377v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1907.07377v1-abstract-full" style="display: none;"> A Controller Area Network (CAN) bus in the vehicles is an efficient standard bus enabling communication between all Electronic Control Units (ECU). However, CAN bus is not enough to protect itself because of lack of security features. To detect suspicious network connections effectively, the intrusion detection system (IDS) is strongly required. Unlike the traditional IDS for Internet, there are small number of known attack signatures for vehicle networks. Also, IDS for vehicle requires high accuracy because any false-positive error can seriously affect the safety of the driver. To solve this problem, we propose a novel IDS model for in-vehicle networks, GIDS (GAN based Intrusion Detection System) using deep-learning model, Generative Adversarial Nets. GIDS can learn to detect unknown attacks using only normal data. As experiment result, GIDS shows high detection accuracy for four unknown attacks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.07377v1-abstract-full').style.display = 'none'; document.getElementById('1907.07377v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 July, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 11 figures, Accepted for PST 2018 : 16th International Conference on Privacy, Security and Trust, some numbers in table 2 has fixed after doing additional experiment</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> In 2018 16th Annual Conference on Privacy, Security and Trust (PST), pp. 1-6. IEEE, 2018 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.09456">arXiv:1906.09456</a> <span> [<a href="https://arxiv.org/pdf/1906.09456">pdf</a>, <a href="https://arxiv.org/ps/1906.09456">ps</a>, <a href="https://arxiv.org/format/1906.09456">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/PST.2018.8514216">10.1109/PST.2018.8514216 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Andro-Simnet: Android Malware Family Classification Using Social Network Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+H+M">Hye Min Kim</a>, <a href="/search/cs?searchtype=author&query=Song%2C+H+M">Hyun Min Song</a>, <a href="/search/cs?searchtype=author&query=Seo%2C+J+W">Jae Woo Seo</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1906.09456v1-abstract-short" style="display: inline;"> While the rapid adaptation of mobile devices changes our daily life more conveniently, the threat derived from malware is also increased. There are lots of research to detect malware to protect mobile devices, but most of them adopt only signature-based malware detection method that can be easily bypassed by polymorphic and metamorphic malware. To detect malware and its variants, it is essential t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.09456v1-abstract-full').style.display = 'inline'; document.getElementById('1906.09456v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.09456v1-abstract-full" style="display: none;"> While the rapid adaptation of mobile devices changes our daily life more conveniently, the threat derived from malware is also increased. There are lots of research to detect malware to protect mobile devices, but most of them adopt only signature-based malware detection method that can be easily bypassed by polymorphic and metamorphic malware. To detect malware and its variants, it is essential to adopt behavior-based detection for efficient malware classification. This paper presents a system that classifies malware by using common behavioral characteristics along with malware families. We measure the similarity between malware families with carefully chosen features commonly appeared in the same family. With the proposed similarity measure, we can classify malware by malware's attack behavior pattern and tactical characteristics. Also, we apply a community detection algorithm to increase the modularity within each malware family network aggregation. To maintain high classification accuracy, we propose a process to derive the optimal weights of the selected features in the proposed similarity measure. During this process, we find out which features are significant for representing the similarity between malware samples. Finally, we provide an intuitive graph visualization of malware samples which is helpful to understand the distribution and likeness of the malware networks. In the experiment, the proposed system achieved 97% accuracy for malware classification and 95% accuracy for prediction by K-fold cross-validation using the real malware dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.09456v1-abstract-full').style.display = 'none'; document.getElementById('1906.09456v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 11 figures, dataset link: http://ocslab.hksecurity.net/Datasets/andro-simnet , demo video: https://youtu.be/JmfS-ZtCbg4 , In Proceedings of the 16th Annual Conference on Privacy, Security and Trust (PST), 2018</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2018 16th Annual Conference on Privacy, Security and Trust (PST), Belfast, 2018, pp. 1-8 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.03568">arXiv:1904.03568</a> <span> [<a href="https://arxiv.org/pdf/1904.03568">pdf</a>, <a href="https://arxiv.org/format/1904.03568">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Active Robot-Assisted Feeding with a General-Purpose Mobile Manipulator: Design, Evaluation, and Lessons Learned </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+D">Daehyung Park</a>, <a href="/search/cs?searchtype=author&query=Hoshi%2C+Y">Yuuna Hoshi</a>, <a href="/search/cs?searchtype=author&query=Mahajan%2C+H+P">Harshal P. Mahajan</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Ho Keun Kim</a>, <a href="/search/cs?searchtype=author&query=Erickson%2C+Z">Zackory Erickson</a>, <a href="/search/cs?searchtype=author&query=Rogers%2C+W+A">Wendy A. Rogers</a>, <a href="/search/cs?searchtype=author&query=Kemp%2C+C+C">Charles C. Kemp</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.03568v2-abstract-short" style="display: inline;"> Eating is an essential activity of daily living (ADL) for staying healthy and living at home independently. Although numerous assistive devices have been introduced, many people with disabilities are still restricted from independent eating due to the devices' physical or perceptual limitations. In this work, we present a new meal-assistance system and evaluations of this system with people with m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.03568v2-abstract-full').style.display = 'inline'; document.getElementById('1904.03568v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.03568v2-abstract-full" style="display: none;"> Eating is an essential activity of daily living (ADL) for staying healthy and living at home independently. Although numerous assistive devices have been introduced, many people with disabilities are still restricted from independent eating due to the devices' physical or perceptual limitations. In this work, we present a new meal-assistance system and evaluations of this system with people with motor impairments. We also discuss learned lessons and design insights based on the evaluations. The meal-assistance system uses a general-purpose mobile manipulator, a Willow Garage PR2, which has the potential to serve as a versatile form of assistive technology. Our active feeding framework enables the robot to autonomously deliver food to the user's mouth, reducing the need for head movement by the user. The user interface, visually-guided behaviors, and safety tools allow people with severe motor impairments to successfully use the system. We evaluated our system with a total of 10 able-bodied participants and 9 participants with motor impairments. Both groups of participants successfully ate various foods using the system and reported high rates of success for the system's autonomous behaviors. In general, participants who operated the system reported that it was comfortable, safe, and easy-to-use. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.03568v2-abstract-full').style.display = 'none'; document.getElementById('1904.03568v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1903.01618">arXiv:1903.01618</a> <span> [<a href="https://arxiv.org/pdf/1903.01618">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1155/2015/479174">10.1155/2015/479174 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Detecting and Classifying Android Malware using Static Analysis along with Creator Information </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+H">Hyunjae Kang</a>, <a href="/search/cs?searchtype=author&query=Jang%2C+J">Jae-wook Jang</a>, <a href="/search/cs?searchtype=author&query=Mohaisen%2C+A">Aziz Mohaisen</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1903.01618v1-abstract-short" style="display: inline;"> Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional under-ground actors, however previous studies overlooked such information as a feature in detecting and classifying malware, and in attributing malware to creators. Guided by this insight, we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.01618v1-abstract-full').style.display = 'inline'; document.getElementById('1903.01618v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1903.01618v1-abstract-full" style="display: none;"> Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional under-ground actors, however previous studies overlooked such information as a feature in detecting and classifying malware, and in attributing malware to creators. Guided by this insight, we propose a method to improve on the performance of Android malware detection by incorporating the creator's information as a feature and classify malicious applications into similar groups. We developed a system that implements this method in practice. Our system enables fast detection of malware by using creator information such as serial number of certificate. Additionally, it analyzes malicious be-haviors and permissions to increase detection accuracy. The system also can classify malware based on similarity scoring. Finally, we showed detection and classification performance with 98% and 90% accuracy respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.01618v1-abstract-full').style.display = 'none'; document.getElementById('1903.01618v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">International Journal of Distributed Sensor Networks</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1812.02361">arXiv:1812.02361</a> <span> [<a href="https://arxiv.org/pdf/1812.02361">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Trustworthy Smart Band: Security Requirement Analysis with Threat Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+S">Suin Kang</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+M">Hye Min Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1812.02361v1-abstract-short" style="display: inline;"> As smart bands make life more convenient and provide a positive lifestyle, many people are now using them. Since smart bands deal with private information, security design and implementation for smart band system become necessary. To make a trustworthy smart band, we must derive the security requirements of the system first, and then design the system satisfying the security requirements. In this… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.02361v1-abstract-full').style.display = 'inline'; document.getElementById('1812.02361v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1812.02361v1-abstract-full" style="display: none;"> As smart bands make life more convenient and provide a positive lifestyle, many people are now using them. Since smart bands deal with private information, security design and implementation for smart band system become necessary. To make a trustworthy smart band, we must derive the security requirements of the system first, and then design the system satisfying the security requirements. In this paper, we apply threat modeling techniques such as Data Flow Diagram, STRIDE, and Attack Tree to the smart band system to identify threats and derive security requirements accordingly. Through threat modeling, we found the vulnerabilities of the smart band system and successfully exploited smart bands with them. To defend against these threats, we propose security measures and verify that they are secure by using Scyther which is a tool for automatic verification of security protocol. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1812.02361v1-abstract-full').style.display = 'none'; document.getElementById('1812.02361v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 December, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.12634">arXiv:1811.12634</a> <span> [<a href="https://arxiv.org/pdf/1811.12634">pdf</a>, <a href="https://arxiv.org/ps/1811.12634">ps</a>, <a href="https://arxiv.org/format/1811.12634">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> ADSaS: Comprehensive Real-time Anomaly Detection System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lee%2C+S">Sooyeon Lee</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1811.12634v1-abstract-short" style="display: inline;"> Since with massive data growth, the need for autonomous and generic anomaly detection system is increased. However, developing one stand-alone generic anomaly detection system that is accurate and fast is still a challenge. In this paper, we propose conventional time-series analysis approaches, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Seasonal Trend decomposition us… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.12634v1-abstract-full').style.display = 'inline'; document.getElementById('1811.12634v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.12634v1-abstract-full" style="display: none;"> Since with massive data growth, the need for autonomous and generic anomaly detection system is increased. However, developing one stand-alone generic anomaly detection system that is accurate and fast is still a challenge. In this paper, we propose conventional time-series analysis approaches, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Seasonal Trend decomposition using Loess (STL), to detect complex and various anomalies. Usually, SARIMA and STL are used only for stationary and periodic time-series, but by combining, we show they can detect anomalies with high accuracy for data that is even noisy and non-periodic. We compared the algorithm to Long Short Term Memory (LSTM), a deep-learning-based algorithm used for anomaly detection system. We used a total of seven real-world datasets and four artificial datasets with different time-series properties to verify the performance of the proposed algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.12634v1-abstract-full').style.display = 'none'; document.getElementById('1811.12634v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 4 figures, In Proceedings of the 19th World Conference on Information Security and Applications (WISA) 2018</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.10050">arXiv:1811.10050</a> <span> [<a href="https://arxiv.org/pdf/1811.10050">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1155/2019/6268476">10.1155/2019/6268476 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Automated Dataset Generation System for Collaborative Research of Cyber Threat Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+D">Daegeon Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1811.10050v3-abstract-short" style="display: inline;"> The objectives of cyberattacks are becoming sophisticated, and attackers are concealing their identity by masquerading as other attackers. Cyber threat intelligence (CTI) is gaining attention as a way to collect meaningful knowledge to better understand the intention of an attacker and eventually predict future attacks. A systemic threat analysis based on data acquired from actual cyber incidents… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.10050v3-abstract-full').style.display = 'inline'; document.getElementById('1811.10050v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.10050v3-abstract-full" style="display: none;"> The objectives of cyberattacks are becoming sophisticated, and attackers are concealing their identity by masquerading as other attackers. Cyber threat intelligence (CTI) is gaining attention as a way to collect meaningful knowledge to better understand the intention of an attacker and eventually predict future attacks. A systemic threat analysis based on data acquired from actual cyber incidents is a useful approach to generating intelligence for such an objective. Developing an analysis technique requires a high volume and fine quality data. However, researchers can become discouraged by an inaccessibility to data because organizations rarely release their data to the research community. Owing to a data inaccessibility issue, academic research tends to be biased toward techniques that develope steps of the CTI process other than analysis and production. In this paper, we propose an automated dataset generation system called CTIMiner. The system collects threat data from publicly available security reports and malware repositories. The data are stored in a structured format. We released the source codes and dataset to the public, including approximately 640,000 records from 612 security reports published from January 2008 to June 2019. In addition, we present a statistical feature of the dataset and techniques that can be developed using it. Moreover, we demonstrate an application example of the dataset that analyzes the correlation and characteristics of an incident. We believe our dataset will promote collaborative research on threat analysis for the generation of CTI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.10050v3-abstract-full').style.display = 'none'; document.getElementById('1811.10050v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">preprint version of paper published in Security and Communication Networks special issue on Data-Driven Cybersecurity</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1805.09956">arXiv:1805.09956</a> <span> [<a href="https://arxiv.org/pdf/1805.09956">pdf</a>, <a href="https://arxiv.org/format/1805.09956">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> Improved Approximation for Node-Disjoint Paths in Grids with Sources on the Boundary </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chuzhoy%2C+J">Julia Chuzhoy</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+H+K">David H. K. Kim</a>, <a href="/search/cs?searchtype=author&query=Nimavat%2C+R">Rachit Nimavat</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1805.09956v1-abstract-short" style="display: inline;"> We study the classical Node-Disjoint Paths (NDP) problem: given an undirected $n$-vertex graph G, together with a set {(s_1,t_1),...,(s_k,t_k)} of pairs of its vertices, called source-destination, or demand pairs, find a maximum-cardinality set of mutually node-disjoint paths that connect the demand pairs. The best current approximation for the problem is achieved by a simple greedy $O(\sqrt{n})$-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.09956v1-abstract-full').style.display = 'inline'; document.getElementById('1805.09956v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1805.09956v1-abstract-full" style="display: none;"> We study the classical Node-Disjoint Paths (NDP) problem: given an undirected $n$-vertex graph G, together with a set {(s_1,t_1),...,(s_k,t_k)} of pairs of its vertices, called source-destination, or demand pairs, find a maximum-cardinality set of mutually node-disjoint paths that connect the demand pairs. The best current approximation for the problem is achieved by a simple greedy $O(\sqrt{n})$-approximation algorithm. A special case of the problem called NDP-Grid, where the underlying graph is a grid, has been studied extensively. The best current approximation algorithm for NDP-Grid achieves an $\tilde{O}(n^{1/4})$-approximation factor. On the negative side, a recent result by the authors shows that NDP is hard to approximate to within factor $2^{惟(\sqrt{\log n})}$, even if the underlying graph is a sub-graph of a grid, and all source vertices lie on the grid boundary. In a follow-up work, the authors further show that NDP-Grid is hard to approximate to within factor $惟(2^{\log^{1-蔚}n})$ for any constant $蔚$ under standard complexity assumptions, and to within factor $n^{惟(1/(\log\log n)^2)}$ under randomized ETH. In this paper we study NDP-Grid, where all source vertices {s_1,...,s_k} appear on the grid boundary. Our main result is an efficient randomized $2^{O(\sqrt{\log n} \cdot \log\log n)}$-approximation algorithm for this problem. We generalize this result to instances where the source vertices lie within a prescribed distance from the grid boundary. Much of the work on approximation algorithms for NDP relies on the multicommodity flow relaxation of the problem, which is known to have an $惟(\sqrt n)$ integrality gap, even in grid graphs. Our work departs from this paradigm, and uses a (completely different) linear program only to select the pairs to be routed, while the routing itself is computed by other methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.09956v1-abstract-full').style.display = 'none'; document.getElementById('1805.09956v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 May, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in the proceedings of ICALP 2018</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> F.2.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1801.06368">arXiv:1801.06368</a> <span> [<a href="https://arxiv.org/pdf/1801.06368">pdf</a>, <a href="https://arxiv.org/format/1801.06368">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> No Silk Road for Online Gamers!: Using Social Network Analysis to Unveil Black Markets in Online Games </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lee%2C+E">Eunjo Lee</a>, <a href="/search/cs?searchtype=author&query=Woo%2C+J">Jiyoung Woo</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H">Hyoungshick Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1801.06368v1-abstract-short" style="display: inline;"> Online game involves a very large number of users who are interconnected and interact with each other via the Internet. We studied the characteristics of exchanging virtual goods with real money through processes called "real money trading (RMT)." This exchange might influence online game user behaviors and cause damage to the reputation of game companies. We examined in-game transactions to revea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.06368v1-abstract-full').style.display = 'inline'; document.getElementById('1801.06368v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1801.06368v1-abstract-full" style="display: none;"> Online game involves a very large number of users who are interconnected and interact with each other via the Internet. We studied the characteristics of exchanging virtual goods with real money through processes called "real money trading (RMT)." This exchange might influence online game user behaviors and cause damage to the reputation of game companies. We examined in-game transactions to reveal RMT by constructing a social graph of virtual goods exchanges in an online game and identifying network communities of users. We analyzed approximately 6,000,000 transactions in a popular online game and inferred RMT transactions by comparing the RMT transactions crawled from an out-game market. Our findings are summarized as follows: (1) the size of the RMT market could be approximately estimated; (2) professional RMT providers typically form a specific network structure (either star-shape or chain) in the trading network, which can be used as a clue for tracing RMT transactions; and (3) the observed RMT market has evolved over time into a monopolized market with a small number of large-sized virtual goods providers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.06368v1-abstract-full').style.display = 'none'; document.getElementById('1801.06368v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 January, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 11 figures, In Proceedings of the 27th International World Wide Web Conference (WWW) 2018</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1711.01980">arXiv:1711.01980</a> <span> [<a href="https://arxiv.org/pdf/1711.01980">pdf</a>, <a href="https://arxiv.org/format/1711.01980">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> Almost Polynomial Hardness of Node-Disjoint Paths in Grids </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chuzhoy%2C+J">Julia Chuzhoy</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+H+K">David H. K. Kim</a>, <a href="/search/cs?searchtype=author&query=Nimavat%2C+R">Rachit Nimavat</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1711.01980v1-abstract-short" style="display: inline;"> In the classical Node-Disjoint Paths (NDP) problem, we are given an $n$-vertex graph $G=(V,E)$, and a collection $M=\{(s_1,t_1),\ldots,(s_k,t_k)\}$ of pairs of its vertices, called source-destination, or demand pairs. The goal is to route as many of the demand pairs as possible, where to route a pair we need to select a path connecting it, so that all selected paths are disjoint in their vertices.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.01980v1-abstract-full').style.display = 'inline'; document.getElementById('1711.01980v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1711.01980v1-abstract-full" style="display: none;"> In the classical Node-Disjoint Paths (NDP) problem, we are given an $n$-vertex graph $G=(V,E)$, and a collection $M=\{(s_1,t_1),\ldots,(s_k,t_k)\}$ of pairs of its vertices, called source-destination, or demand pairs. The goal is to route as many of the demand pairs as possible, where to route a pair we need to select a path connecting it, so that all selected paths are disjoint in their vertices. The best current algorithm for NDP achieves an $O(\sqrt{n})$-approximation, while, until recently, the best negative result was a factor $惟(\log^{1/2-蔚}n)$-hardness of approximation, for any constant $蔚$, unless $NP \subseteq ZPTIME(n^{poly \log n})$. In a recent work, the authors have shown an improved $2^{惟(\sqrt{\log n})}$-hardness of approximation for NDP, unless $NP\subseteq DTIME(n^{O(\log n)})$, even if the underlying graph is a subgraph of a grid graph, and all source vertices lie on the boundary of the grid. Unfortunately, this result does not extend to grid graphs. The approximability of the NDP problem on grid graphs has remained a tantalizing open question, with the best current upper bound of $\tilde{O}(n^{1/4})$, and the best current lower bound of APX-hardness. In this paper we come close to resolving the approximability of NDP in general, and NDP in grids in particular. Our main result is that NDP is $2^{惟(\log^{1-蔚} n)}$-hard to approximate for any constant $蔚$, assuming that $NP\not\subseteq RTIME(n^{poly\log n})$, and that it is $n^{惟(1/(\log \log n)^2)}$-hard to approximate, assuming that for some constant $未>0$, $NP \not \subseteq RTIME(2^{n^未})$. These results hold even for grid graphs and wall graphs, and extend to the closely related Edge-Disjoint Paths problem, even in wall graphs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1711.01980v1-abstract-full').style.display = 'none'; document.getElementById('1711.01980v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.00242">arXiv:1705.00242</a> <span> [<a href="https://arxiv.org/pdf/1705.00242">pdf</a>, <a href="https://arxiv.org/format/1705.00242">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Crime Scene Re-investigation: A Postmortem Analysis of Game Account Stealers' Behaviors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+H">Hana Kim</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+S">Seongil Yang</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.00242v1-abstract-short" style="display: inline;"> As item trading becomes more popular, users can change their game items or money into real money more easily. At the same time, hackers turn their eyes on stealing other users game items or money because it is much easier to earn money than traditional gold-farming by running game bots. Game companies provide various security measures to block account- theft attempts, but many security measures on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.00242v1-abstract-full').style.display = 'inline'; document.getElementById('1705.00242v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.00242v1-abstract-full" style="display: none;"> As item trading becomes more popular, users can change their game items or money into real money more easily. At the same time, hackers turn their eyes on stealing other users game items or money because it is much easier to earn money than traditional gold-farming by running game bots. Game companies provide various security measures to block account- theft attempts, but many security measures on the user-side are disregarded by users because of lack of usability. In this study, we propose a server-side account theft detection system base on action sequence analysis to protect game users from malicious hackers. We tested this system in the real Massively Multiplayer Online Role Playing Game (MMORPG). By analyzing users full game play log, our system can find the particular action sequences of hackers with high accuracy. Also, we can trace where the victim accounts stolen money goes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.00242v1-abstract-full').style.display = 'none'; document.getElementById('1705.00242v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 8 figures, In Proceedings of the 15th Annual Workshop on Network and Systems Support for Games (NetGames 2017)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> K.6.5; K.8.0 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1705.00128">arXiv:1705.00128</a> <span> [<a href="https://arxiv.org/pdf/1705.00128">pdf</a>, <a href="https://arxiv.org/format/1705.00128">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Evaluating Security and Availability of Multiple Redundancy Designs when Applying Security Patches </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ge%2C+M">Mengmeng Ge</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+S">Dong Seong Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1705.00128v1-abstract-short" style="display: inline;"> In most of modern enterprise systems, redundancy configuration is often considered to provide availability during the part of such systems is being patched. However, the redundancy may increase the attack surface of the system. In this paper, we model and assess the security and capacity oriented availability of multiple server redundancy designs when applying security patches to the servers. We c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.00128v1-abstract-full').style.display = 'inline'; document.getElementById('1705.00128v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1705.00128v1-abstract-full" style="display: none;"> In most of modern enterprise systems, redundancy configuration is often considered to provide availability during the part of such systems is being patched. However, the redundancy may increase the attack surface of the system. In this paper, we model and assess the security and capacity oriented availability of multiple server redundancy designs when applying security patches to the servers. We construct (1) a graphical security model to evaluate the security under potential attacks before and after applying patches, (2) a stochastic reward net model to assess the capacity oriented availability of the system with a patch schedule. We present our approach based on case study and model-based evaluation for multiple design choices. The results show redundancy designs increase capacity oriented availability but decrease security when applying security patches. We define functions that compare values of security metrics and capacity oriented availability with the chosen upper/lower bounds to find design choices that satisfy both security and availability requirements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1705.00128v1-abstract-full').style.display = 'none'; document.getElementById('1705.00128v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1704.05223">arXiv:1704.05223</a> <span> [<a href="https://arxiv.org/pdf/1704.05223">pdf</a>, <a href="https://arxiv.org/format/1704.05223">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Know Your Master: Driver Profiling-based Anti-theft Method </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kwak%2C+B+I">Byung Il Kwak</a>, <a href="/search/cs?searchtype=author&query=Woo%2C+J">JiYoung Woo</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1704.05223v1-abstract-short" style="display: inline;"> Although many anti-theft technologies are implemented, auto-theft is still increasing. Also, security vulnerabilities of cars can be used for auto-theft by neutralizing anti-theft system. This keyless auto-theft attack will be increased as cars adopt computerized electronic devices more. To detect auto-theft efficiently, we propose the driver verification method that analyzes driving patterns usin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1704.05223v1-abstract-full').style.display = 'inline'; document.getElementById('1704.05223v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1704.05223v1-abstract-full" style="display: none;"> Although many anti-theft technologies are implemented, auto-theft is still increasing. Also, security vulnerabilities of cars can be used for auto-theft by neutralizing anti-theft system. This keyless auto-theft attack will be increased as cars adopt computerized electronic devices more. To detect auto-theft efficiently, we propose the driver verification method that analyzes driving patterns using measurements from the sensor in the vehicle. In our model, we add mechanical features of automotive parts that are excluded in previous works, but can be differentiated by drivers' driving behaviors. We design the model that uses significant features through feature selection to reduce the time cost of feature processing and improve the detection performance. Further, we enrich the feature set by deriving statistical features such as mean, median, and standard deviation. This minimizes the effect of fluctuation of feature values per driver and finally generates the reliable model. We also analyze the effect of the size of sliding window on performance to detect the time point when the detection becomes reliable and to inform owners the theft event as soon as possible. We apply our model with real driving and show the contribution of our work to the literature of driver identification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1704.05223v1-abstract-full').style.display = 'none'; document.getElementById('1704.05223v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 April, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 11 figures, Accepted for PST 2016 : 14th International Conference on Privacy, Security and Trust</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.01500">arXiv:1703.01500</a> <span> [<a href="https://arxiv.org/pdf/1703.01500">pdf</a>, <a href="https://arxiv.org/format/1703.01500">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3038912.3038914">10.1145/3038912.3038914 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> I Would Not Plant Apple Trees If the World Will Be Wiped: Analyzing Hundreds of Millions of Behavioral Records of Players During an MMORPG Beta Test </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+A+R">Ah Reum Kang</a>, <a href="/search/cs?searchtype=author&query=Blackburn%2C+J">Jeremy Blackburn</a>, <a href="/search/cs?searchtype=author&query=Kwak%2C+H">Haewoon Kwak</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1703.01500v1-abstract-short" style="display: inline;"> In this work, we use player behavior during the closed beta test of the MMORPG ArcheAge as a proxy for an extreme situation: at the end of the closed beta test, all user data is deleted, and thus, the outcome (or penalty) of players' in-game behaviors in the last few days loses its meaning. We analyzed 270 million records of player behavior in the 4th closed beta test of ArcheAge. Our findings sho… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.01500v1-abstract-full').style.display = 'inline'; document.getElementById('1703.01500v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.01500v1-abstract-full" style="display: none;"> In this work, we use player behavior during the closed beta test of the MMORPG ArcheAge as a proxy for an extreme situation: at the end of the closed beta test, all user data is deleted, and thus, the outcome (or penalty) of players' in-game behaviors in the last few days loses its meaning. We analyzed 270 million records of player behavior in the 4th closed beta test of ArcheAge. Our findings show that there are no apparent pandemic behavior changes, but some outliers were more likely to exhibit anti-social behavior (e.g., player killing). We also found that contrary to the reassuring adage that "Even if I knew the world would go to pieces tomorrow, I would still plant my apple tree," players abandoned character progression, showing a drastic decrease in quest completion, leveling, and ability changes at the end of the beta test. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.01500v1-abstract-full').style.display = 'none'; document.getElementById('1703.01500v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 9 figures, In Proceedings of the 26th International World Wide Web Conference (WWW) 2017</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> J.4; K.4 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1611.05429">arXiv:1611.05429</a> <span> [<a href="https://arxiv.org/pdf/1611.05429">pdf</a>, <a href="https://arxiv.org/format/1611.05429">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> New Hardness Results for Routing on Disjoint Paths </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chuzhoy%2C+J">Julia Chuzhoy</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+H+K">David H. K. Kim</a>, <a href="/search/cs?searchtype=author&query=Nimavat%2C+R">Rachit Nimavat</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1611.05429v1-abstract-short" style="display: inline;"> In the classical Node-Disjoint Paths (NDP) problem, the input consists of an undirected $n$-vertex graph $G$, and a collection $\mathcal{M}=\{(s_1,t_1),\ldots,(s_k,t_k)\}$ of pairs of its vertices, called source-destination, or demand, pairs. The goal is to route the largest possible number of the demand pairs via node-disjoint paths. The best current approximation for the problem is achieved by a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1611.05429v1-abstract-full').style.display = 'inline'; document.getElementById('1611.05429v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1611.05429v1-abstract-full" style="display: none;"> In the classical Node-Disjoint Paths (NDP) problem, the input consists of an undirected $n$-vertex graph $G$, and a collection $\mathcal{M}=\{(s_1,t_1),\ldots,(s_k,t_k)\}$ of pairs of its vertices, called source-destination, or demand, pairs. The goal is to route the largest possible number of the demand pairs via node-disjoint paths. The best current approximation for the problem is achieved by a simple greedy algorithm, whose approximation factor is $O(\sqrt n)$, while the best current negative result is an $惟(\log^{1/2-未}n)$-hardness of approximation for any constant $未$, under standard complexity assumptions. Even seemingly simple special cases of the problem are still poorly understood: when the input graph is a grid, the best current algorithm achieves an $\tilde O(n^{1/4})$-approximation, and when it is a general planar graph, the best current approximation ratio of an efficient algorithm is $\tilde O(n^{9/19})$. The best currently known lower bound on the approximability of both these versions of the problem is APX-hardness. In this paper we prove that NDP is $2^{惟(\sqrt{\log n})}$-hard to approximate, unless all problems in NP have algorithms with running time $n^{O(\log n)}$. Our result holds even when the underlying graph is a planar graph with maximum vertex degree $3$, and all source vertices lie on the boundary of a single face (but the destination vertices may lie anywhere in the graph). We extend this result to the closely related Edge-Disjoint Paths problem, showing the same hardness of approximation ratio even for sub-cubic planar graphs with all sources lying on the boundary of a single face. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1611.05429v1-abstract-full').style.display = 'none'; document.getElementById('1611.05429v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2016. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1606.01971">arXiv:1606.01971</a> <span> [<a href="https://arxiv.org/pdf/1606.01971">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jang%2C+J">Jae-wook Jang</a>, <a href="/search/cs?searchtype=author&query=Woo%2C+J">Jiyoung Woo</a>, <a href="/search/cs?searchtype=author&query=Mohaisen%2C+A">Aziz Mohaisen</a>, <a href="/search/cs?searchtype=author&query=Yun%2C+J">Jaesung Yun</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1606.01971v1-abstract-short" style="display: inline;"> As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using feat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01971v1-abstract-full').style.display = 'inline'; document.getElementById('1606.01971v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.01971v1-abstract-full" style="display: none;"> As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that influence-based graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01971v1-abstract-full').style.display = 'none'; document.getElementById('1606.01971v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 June, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Mathematical Problems in Engineering, Vol 2015</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1606.01426">arXiv:1606.01426</a> <span> [<a href="https://arxiv.org/pdf/1606.01426">pdf</a>, <a href="https://arxiv.org/format/1606.01426">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Multimodal Game Bot Detection using User Behavioral Characteristics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+A+R">Ah Reum Kang</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+S+H">Seong Hoon Jeong</a>, <a href="/search/cs?searchtype=author&query=Mohaisen%2C+A">Aziz Mohaisen</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+H+K">Huy Kang Kim</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1606.01426v1-abstract-short" style="display: inline;"> As the online service industry has continued to grow, illegal activities in the online world have drastically increased and become more diverse. Most illegal activities occur continuously because cyber assets, such as game items and cyber money in online games, can be monetized into real currency. The aim of this study is to detect game bots in a Massively Multiplayer Online Role Playing Game (MMO… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01426v1-abstract-full').style.display = 'inline'; document.getElementById('1606.01426v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1606.01426v1-abstract-full" style="display: none;"> As the online service industry has continued to grow, illegal activities in the online world have drastically increased and become more diverse. Most illegal activities occur continuously because cyber assets, such as game items and cyber money in online games, can be monetized into real currency. The aim of this study is to detect game bots in a Massively Multiplayer Online Role Playing Game (MMORPG). We observed the behavioral characteristics of game bots and found that they execute repetitive tasks associated with gold farming and real money trading. We propose a game bot detection methodology based on user behavioral characteristics. The methodology of this paper was applied to real data provided by a major MMORPG company. Detection accuracy rate increased to 96.06% on the banned account list. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1606.01426v1-abstract-full').style.display = 'none'; document.getElementById('1606.01426v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 June, 2016; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2016. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Springerplus. 2016; 5: 523 </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Kim%2C+H+K&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a 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