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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <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/2410.21337">arXiv:2410.21337</a> <span>  </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</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"> Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rahman%2C+M+A">Md Abdur Rahman</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+F">Fan Wu</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</a>, <a href="/search/cs?searchtype=author&query=Ahamed%2C+S+I">Sheikh Iqbal Ahamed</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="2410.21337v2-abstract-short" style="display: inline;"> Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. These attacks target LLMs applications through using carefully designed input prompts to divert the model from adhering to origin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21337v2-abstract-full').style.display = 'inline'; document.getElementById('2410.21337v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.21337v2-abstract-full" style="display: none;"> Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. These attacks target LLMs applications through using carefully designed input prompts to divert the model from adhering to original instruction, thereby it could execute unintended actions. These manipulations pose serious security threats which potentially results in data leaks, biased outputs, or harmful responses. This project explores the security vulnerabilities in relation to prompt injection attacks. To detect whether a prompt is vulnerable or not, we follows two approaches: 1) a pre-trained LLM, and 2) a fine-tuned LLM. Then, we conduct a thorough analysis and comparison of the classification performance. Firstly, we use pre-trained XLM-RoBERTa model to detect prompt injections using test dataset without any fine-tuning and evaluate it by zero-shot classification. Then, this proposed work will apply supervised fine-tuning to this pre-trained LLM using a task-specific labeled dataset from deepset in huggingface, and this fine-tuned model achieves impressive results with 99.13\% accuracy, 100\% precision, 98.33\% recall and 99.15\% F1-score thorough rigorous experimentation and evaluation. We observe that our approach is highly efficient in detecting prompt injection attacks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21337v2-abstract-full').style.display = 'none'; document.getElementById('2410.21337v2-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> 7 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">I am requesting the withdrawal of my paper due to critical issues identified in the methodology/results that may impact its accuracy and reliability. I also plan to make substantial revisions that go beyond minor corrections</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20664">arXiv:2410.20664</a> <span>  </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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Embedding with Large Language Models for Classification of HIPAA Safeguard Compliance Rules </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rahman%2C+M+A">Md Abdur Rahman</a>, <a href="/search/cs?searchtype=author&query=Barek%2C+M+A">Md Abdul Barek</a>, <a href="/search/cs?searchtype=author&query=Riad%2C+A+K+I">ABM Kamrul Islam Riad</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+M+M">Md Mostafizur Rahman</a>, <a href="/search/cs?searchtype=author&query=Rashid%2C+M+B">Md Bajlur Rashid</a>, <a href="/search/cs?searchtype=author&query=Ambedkar%2C+S">Smita Ambedkar</a>, <a href="/search/cs?searchtype=author&query=Miaa%2C+M+R">Md Raihan Miaa</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+F">Fan Wu</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</a>, <a href="/search/cs?searchtype=author&query=Ahamed%2C+S+I">Sheikh Iqbal Ahamed</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="2410.20664v2-abstract-short" style="display: inline;"> Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20664v2-abstract-full').style.display = 'inline'; document.getElementById('2410.20664v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20664v2-abstract-full" style="display: none;"> Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to various machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95\%. Additionally, we obtained high accuracy from Support Vector Machine (99.79\%), Random Forest (99.73\%), and Naive Bayes (95.93\%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20664v2-abstract-full').style.display = 'none'; document.getElementById('2410.20664v2-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> 7 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">I am requesting the withdrawal of my paper due to critical issues identified in the methodology/results that may impact its accuracy and reliability. I also plan to make substantial revisions that go beyond minor corrections</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.17668">arXiv:2409.17668</a> <span> [<a href="https://arxiv.org/pdf/2409.17668">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> A Database Engineered System for Big Data Analytics on Tornado Climatology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bian%2C+F">Fengfan Bian</a>, <a href="/search/cs?searchtype=author&query=Leung%2C+C+K">Carson K. Leung</a>, <a href="/search/cs?searchtype=author&query=Grenier%2C+P">Piers Grenier</a>, <a href="/search/cs?searchtype=author&query=Pu%2C+H">Harry Pu</a>, <a href="/search/cs?searchtype=author&query=Ning%2C+S">Samuel Ning</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</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="2409.17668v1-abstract-short" style="display: inline;"> Recognizing the challenges with current tornado warning systems, we investigate alternative approaches. In particular, we present a database engi-neered system that integrates information from heterogeneous rich data sources, including climatology data for tornadoes and data just before a tornado warning. The system aids in predicting tornado occurrences by identifying the data points that form th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17668v1-abstract-full').style.display = 'inline'; document.getElementById('2409.17668v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.17668v1-abstract-full" style="display: none;"> Recognizing the challenges with current tornado warning systems, we investigate alternative approaches. In particular, we present a database engi-neered system that integrates information from heterogeneous rich data sources, including climatology data for tornadoes and data just before a tornado warning. The system aids in predicting tornado occurrences by identifying the data points that form the basis of a tornado warning. Evaluation on US data highlights the advantages of using a classification forecasting recurrent neural network (RNN) model. The results highlight the effectiveness of our database engineered system for big data analytics on tornado climatology-especially, in accurately predict-ing tornado lead-time, magnitude, and location, contributing to the development of sustainable cities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17668v1-abstract-full').style.display = 'none'; document.getElementById('2409.17668v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.13331">arXiv:2409.13331</a> <span> [<a href="https://arxiv.org/pdf/2409.13331">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</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"> Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Rahman%2C+M+A">Md Abdur Rahman</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+F">Fan Wu</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</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="2409.13331v1-abstract-short" style="display: inline;"> Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approach… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13331v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13331v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13331v1-abstract-full" style="display: none;"> Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13331v1-abstract-full').style.display = 'none'; document.getElementById('2409.13331v1-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> 20 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.18604">arXiv:2407.18604</a> <span> [<a href="https://arxiv.org/pdf/2407.18604">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</span> </div> </div> <p class="title is-5 mathjax"> Turning Multidimensional Big Data Analytics into Practice: Design and Implementation of ClustCube Big-Data Tools in Real-Life Scenarios </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</a>, <a href="/search/cs?searchtype=author&query=Hafsaoui%2C+A">Abderraouf Hafsaoui</a>, <a href="/search/cs?searchtype=author&query=Benlaredj%2C+I">Ismail Benlaredj</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="2407.18604v1-abstract-short" style="display: inline;"> Multidimensional Big Data Analytics is an emerging area that marries the capabilities of OLAP with modern Big Data Analytics. Essentially, the idea is engrafting multidimensional models into Big Data analytics processes to gain into expressive power of the overall discovery task. ClustCube is a state-of-the-art model that combines OLAP and Clustering, thus delving into practical and well-understoo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18604v1-abstract-full').style.display = 'inline'; document.getElementById('2407.18604v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.18604v1-abstract-full" style="display: none;"> Multidimensional Big Data Analytics is an emerging area that marries the capabilities of OLAP with modern Big Data Analytics. Essentially, the idea is engrafting multidimensional models into Big Data analytics processes to gain into expressive power of the overall discovery task. ClustCube is a state-of-the-art model that combines OLAP and Clustering, thus delving into practical and well-understood advantages in the context of real-life applications and systems. In this paper, we show how ClustCube can effectively and efficiently realizing nice tools for supporting Multidimensional Big Data Analytics, and assess these tools in the context of real-life research projects. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18604v1-abstract-full').style.display = 'none'; document.getElementById('2407.18604v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.09722">arXiv:2308.09722</a> <span> [<a href="https://arxiv.org/pdf/2308.09722">pdf</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="Computation and Language">cs.CL</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"> A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akter%2C+M+S">Mst Shapna Akter</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</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="2308.09722v1-abstract-short" style="display: inline;"> Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to addres… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.09722v1-abstract-full').style.display = 'inline'; document.getElementById('2308.09722v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.09722v1-abstract-full" style="display: none;"> Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.09722v1-abstract-full').style.display = 'none'; document.getElementById('2308.09722v1-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> 15 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">arXiv admin note: text overlap with arXiv:2303.07484</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.08060">arXiv:2306.08060</a> <span> [<a href="https://arxiv.org/pdf/2306.08060">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> <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="Quantum Physics">quant-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/10.1109/BigData55660.2022.10020813">10.1109/BigData55660.2022.10020813 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akter%2C+M+S">Mst Shapna Akter</a>, <a href="/search/cs?searchtype=author&query=Faruk%2C+M+J+H">Md Jobair Hossain Faruk</a>, <a href="/search/cs?searchtype=author&query=Anjum%2C+N">Nafisa Anjum</a>, <a href="/search/cs?searchtype=author&query=Masum%2C+M">Mohammad Masum</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+A">Akond Rahman</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+F">Fan Wu</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</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="2306.08060v1-abstract-short" style="display: inline;"> The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.08060v1-abstract-full').style.display = 'inline'; document.getElementById('2306.08060v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.08060v1-abstract-full" style="display: none;"> The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.08060v1-abstract-full').style.display = 'none'; document.getElementById('2306.08060v1-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> 31 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.07981">arXiv:2306.07981</a> <span> [<a href="https://arxiv.org/pdf/2306.07981">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> <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="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akter%2C+M+S">Mst Shapna Akter</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Cardenas%2C+J+R">Juan Rodriguez Cardenas</a>, <a href="/search/cs?searchtype=author&query=Ahamed%2C+S+I">Sheikh Iqbal Ahamed</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</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="2306.07981v1-abstract-short" style="display: inline;"> One of the most significant challenges in the field of software code auditing is the presence of vulnerabilities in software source code. Every year, more and more software flaws are discovered, either internally in proprietary code or publicly disclosed. These flaws are highly likely to be exploited and can lead to system compromise, data leakage, or denial of service. To create a large-scale mac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.07981v1-abstract-full').style.display = 'inline'; document.getElementById('2306.07981v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.07981v1-abstract-full" style="display: none;"> One of the most significant challenges in the field of software code auditing is the presence of vulnerabilities in software source code. Every year, more and more software flaws are discovered, either internally in proprietary code or publicly disclosed. These flaws are highly likely to be exploited and can lead to system compromise, data leakage, or denial of service. To create a large-scale machine learning system for function level vulnerability identification, we utilized a sizable dataset of C and C++ open-source code containing millions of functions with potential buffer overflow exploits. We have developed an efficient and scalable vulnerability detection method based on neural network models that learn features extracted from the source codes. The source code is first converted into an intermediate representation to remove unnecessary components and shorten dependencies. We maintain the semantic and syntactic information using state of the art word embedding algorithms such as GloVe and fastText. The embedded vectors are subsequently fed into neural networks such as LSTM, BiLSTM, LSTM Autoencoder, word2vec, BERT, and GPT2 to classify the possible vulnerabilities. We maintain the semantic and syntactic information using state of the art word embedding algorithms such as GloVe and fastText. The embedded vectors are subsequently fed into neural networks such as LSTM, BiLSTM, LSTM Autoencoder, word2vec, BERT, and GPT2 to classify the possible vulnerabilities. Furthermore, we have proposed a neural network model that can overcome issues associated with traditional neural networks. We have used evaluation metrics such as F1 score, precision, recall, accuracy, and total execution time to measure the performance. We have conducted a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.07981v1-abstract-full').style.display = 'none'; document.getElementById('2306.07981v1-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> 31 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.00283">arXiv:2306.00283</a> <span> [<a href="https://arxiv.org/pdf/2306.00283">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akter%2C+M+S">Mst Shapna Akter</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</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="2306.00283v1-abstract-short" style="display: inline;"> Neural network approaches are machine learning methods that are widely used in various domains, such as healthcare and cybersecurity. Neural networks are especially renowned for their ability to deal with image datasets. During the training process with images, various fundamental mathematical operations are performed in the neural network. These operations include several algebraic and mathematic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.00283v1-abstract-full').style.display = 'inline'; document.getElementById('2306.00283v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.00283v1-abstract-full" style="display: none;"> Neural network approaches are machine learning methods that are widely used in various domains, such as healthcare and cybersecurity. Neural networks are especially renowned for their ability to deal with image datasets. During the training process with images, various fundamental mathematical operations are performed in the neural network. These operations include several algebraic and mathematical functions, such as derivatives, convolutions, and matrix inversions and transpositions. Such operations demand higher processing power than what is typically required for regular computer usage. Since CPUs are built with serial processing, they are not appropriate for handling large image datasets. On the other hand, GPUs have parallel processing capabilities and can provide higher speed. This paper utilizes advanced neural network techniques, such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST VGG16, and our proposed models, to compare CPU and GPU resources. We implemented a system for classifying Autism disease using face images of autistic and non-autistic children to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It was observed that GPU outperformed CPU in all tests conducted. Moreover, the performance of the neural network models in terms of accuracy increased on GPU compared to CPU. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.00283v1-abstract-full').style.display = 'none'; document.getElementById('2306.00283v1-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> 31 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.07520">arXiv:2303.07520</a> <span> [<a href="https://arxiv.org/pdf/2303.07520">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</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/BigData55660.2022.10020302">10.1109/BigData55660.2022.10020302 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akter%2C+M+S">Mst Shapna Akter</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Sneha%2C+S">Sweta Sneha</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</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="2303.07520v1-abstract-short" style="display: inline;"> Skin cancer detection is challenging since different types of skin lesions share high similarities. This paper proposes a computer-based deep learning approach that will accurately identify different kinds of skin lesions. Deep learning approaches can detect skin cancer very accurately since the models learn each pixel of an image. Sometimes humans can get confused by the similarities of the skin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.07520v1-abstract-full').style.display = 'inline'; document.getElementById('2303.07520v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.07520v1-abstract-full" style="display: none;"> Skin cancer detection is challenging since different types of skin lesions share high similarities. This paper proposes a computer-based deep learning approach that will accurately identify different kinds of skin lesions. Deep learning approaches can detect skin cancer very accurately since the models learn each pixel of an image. Sometimes humans can get confused by the similarities of the skin lesions, which we can minimize by involving the machine. However, not all deep learning approaches can give better predictions. Some deep learning models have limitations, leading the model to a false-positive result. We have introduced several deep learning models to classify skin lesions to distinguish skin cancer from different types of skin lesions. Before classifying the skin lesions, data preprocessing and data augmentation methods are used. Finally, a Convolutional Neural Network (CNN) model and six transfer learning models such as Resnet-50, VGG-16, Densenet, Mobilenet, Inceptionv3, and Xception are applied to the publically available benchmark HAM10000 dataset to classify seven classes of skin lesions and to conduct a comparative analysis. The models will detect skin cancer by differentiating the cancerous cell from the non-cancerous ones. The models performance is measured using performance metrics such as precision, recall, f1 score, and accuracy. We receive accuracy of 90, 88, 88, 87, 82, and 77 percent for inceptionv3, Xception, Densenet, Mobilenet, Resnet, CNN, and VGG16, respectively. Furthermore, we develop five different stacking models such as inceptionv3-inceptionv3, Densenet-mobilenet, inceptionv3-Xception, Resnet50-Vgg16, and stack-six for classifying the skin lesions and found that the stacking models perform poorly. We achieve the highest accuracy of 78 percent among all the stacking models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.07520v1-abstract-full').style.display = 'none'; document.getElementById('2303.07520v1-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> 13 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.07514">arXiv:2303.07514</a> <span> [<a href="https://arxiv.org/pdf/2303.07514">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</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/BigData55660.2022.10021025">10.1109/BigData55660.2022.10021025 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akter%2C+M+S">Mst Shapna Akter</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+N">Nova Ahmed</a>, <a href="/search/cs?searchtype=author&query=Leung%2C+C">Carson Leung</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="2303.07514v1-abstract-short" style="display: inline;"> A handwritten word recognition system comes with issues such as lack of large and diverse datasets. It is necessary to resolve such issues since millions of official documents can be digitized by training deep learning models using a large and diverse dataset. Due to the lack of data availability, the trained model does not give the expected result. Thus, it has a high chance of showing poor resul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.07514v1-abstract-full').style.display = 'inline'; document.getElementById('2303.07514v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.07514v1-abstract-full" style="display: none;"> A handwritten word recognition system comes with issues such as lack of large and diverse datasets. It is necessary to resolve such issues since millions of official documents can be digitized by training deep learning models using a large and diverse dataset. Due to the lack of data availability, the trained model does not give the expected result. Thus, it has a high chance of showing poor results. This paper proposes a novel way of generating diverse handwritten word images using handwritten characters. The idea of our project is to train the BiLSTM-CTC architecture with generated synthetic handwritten words. The whole approach shows the process of generating two types of large and diverse handwritten word datasets: overlapped and non-overlapped. Since handwritten words also have issues like overlapping between two characters, we have tried to put it into our experimental part. We have also demonstrated the process of recognizing handwritten documents using the deep learning model. For the experiments, we have targeted the Bangla language, which lacks the handwritten word dataset, and can be followed for any language. Our approach is less complex and less costly than traditional GAN models. Finally, we have evaluated our model using Word Error Rate (WER), accuracy, f1-score, precision, and recall metrics. The model gives 39% WER score, 92% percent accuracy, and 92% percent f1 scores using non-overlapped data and 63% percent WER score, 83% percent accuracy, and 85% percent f1 scores using overlapped data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.07514v1-abstract-full').style.display = 'none'; document.getElementById('2303.07514v1-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> 13 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.07484">arXiv:2303.07484</a> <span> [<a href="https://arxiv.org/pdf/2303.07484">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</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/BigData55660.2022.10020249">10.1109/BigData55660.2022.10020249 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Akter%2C+M+S">Mst Shapna Akter</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+N">Nova Ahmed</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</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="2303.07484v1-abstract-short" style="display: inline;"> Aggressive comments on social media negatively impact human life. Such offensive contents are responsible for depression and suicidal-related activities. Since online social networking is increasing day by day, the hate content is also increasing. Several investigations have been done on the domain of cyberbullying, cyberaggression, hate speech, etc. The majority of the inquiry has been done in th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.07484v1-abstract-full').style.display = 'inline'; document.getElementById('2303.07484v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.07484v1-abstract-full" style="display: none;"> Aggressive comments on social media negatively impact human life. Such offensive contents are responsible for depression and suicidal-related activities. Since online social networking is increasing day by day, the hate content is also increasing. Several investigations have been done on the domain of cyberbullying, cyberaggression, hate speech, etc. The majority of the inquiry has been done in the English language. Some languages (Hindi and Bangla) still lack proper investigations due to the lack of a dataset. This paper particularly worked on the Hindi, Bangla, and English datasets to detect aggressive comments and have shown a novel way of generating machine-translated data to resolve data unavailability issues. A fully machine-translated English dataset has been analyzed with the models such as the Long Short term memory model (LSTM), Bidirectional Long-short term memory model (BiLSTM), LSTM-Autoencoder, word2vec, Bidirectional Encoder Representations from Transformers (BERT), and generative pre-trained transformer (GPT-2) to make an observation on how the models perform on a machine-translated noisy dataset. We have compared the performance of using the noisy data with two more datasets such as raw data, which does not contain any noises, and semi-noisy data, which contains a certain amount of noisy data. We have classified both the raw and semi-noisy data using the aforementioned models. To evaluate the performance of the models, we have used evaluation metrics such as F1-score,accuracy, precision, and recall. We have achieved the highest accuracy on raw data using the gpt2 model, semi-noisy data using the BERT model, and fully machine-translated data using the BERT model. Since many languages do not have proper data availability, our approach will help researchers create machine-translated datasets for several analysis purposes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.07484v1-abstract-full').style.display = 'none'; document.getElementById('2303.07484v1-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> 13 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.09902">arXiv:2207.09902</a> <span> [<a href="https://arxiv.org/pdf/2207.09902">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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/BigData52589.2021.9671576">10.1109/BigData52589.2021.9671576 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Masum%2C+M">Mohammad Masum</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Haddad%2C+H">Hisham Haddad</a>, <a href="/search/cs?searchtype=author&query=Faruk%2C+M+J+H">Md Jobair Hossain Faruk</a>, <a href="/search/cs?searchtype=author&query=Valero%2C+M">Maria Valero</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+M+A">Md Abdullah Khan</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+M+A">Mohammad A. Rahman</a>, <a href="/search/cs?searchtype=author&query=Adnan%2C+M+I">Muhaiminul I. Adnan</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</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="2207.09902v1-abstract-short" style="display: inline;"> Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, ti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09902v1-abstract-full').style.display = 'inline'; document.getElementById('2207.09902v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.09902v1-abstract-full" style="display: none;"> Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework's effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09902v1-abstract-full').style.display = 'none'; document.getElementById('2207.09902v1-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> 7 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">Journal ref:</span> 2021 IEEE International Conference on Big Data (Big Data) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.03525">arXiv:2207.03525</a> <span> [<a href="https://arxiv.org/pdf/2207.03525">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</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="Databases">cs.DB</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/BigData52589.2021.9671379">10.1109/BigData52589.2021.9671379 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Ride-Hailing for Autonomous Vehicles: Hyperledger Fabric-Based Secure and Decentralize Blockchain Platform </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shivers%2C+R">Ryan Shivers</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+M+A">Mohammad Ashiqur Rahman</a>, <a href="/search/cs?searchtype=author&query=Faruk%2C+M+J+H">Md Jobair Hossain Faruk</a>, <a href="/search/cs?searchtype=author&query=Shahriar%2C+H">Hossain Shahriar</a>, <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</a>, <a href="/search/cs?searchtype=author&query=Clincy%2C+V">Victor Clincy</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="2207.03525v1-abstract-short" style="display: inline;"> Ride-hailing and ride-sharing applications have recently gained popularity as a convenient alternative to traditional modes of travel. Current research into autonomous vehicles is accelerating rapidly and will soon become a critical component of a ride-hailing platforms architecture. Implementing an autonomous vehicle ride-hailing platform proves a difficult challenge due to the centralized nature… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.03525v1-abstract-full').style.display = 'inline'; document.getElementById('2207.03525v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.03525v1-abstract-full" style="display: none;"> Ride-hailing and ride-sharing applications have recently gained popularity as a convenient alternative to traditional modes of travel. Current research into autonomous vehicles is accelerating rapidly and will soon become a critical component of a ride-hailing platforms architecture. Implementing an autonomous vehicle ride-hailing platform proves a difficult challenge due to the centralized nature of traditional ride-hailing architectures. In a traditional ride-hailing environment the drivers operate their own personal vehicles so it follows that a fleet of autonomous vehicles would be required for a centralized ride-hailing platform to succeed. Decentralization of the ride-hailing platform would remove a roadblock along the way to an autonomous vehicle ride-hailing platform by allowing owners of autonomous vehicles to add their vehicles to a community-driven fleet when not in use. Blockchain technology is an attractive choice for this decentralized architecture due to its immutability and fault tolerance. This thesis proposes a framework for developing a decentralized ride-hailing architecture that is verifiably secure. This framework is implemented on the Hyperledger Fabric blockchain platform. The evaluation of the implementation is done by applying known security models, utilizing a static analysis tool, and performing a performance analysis under heavy network load. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.03525v1-abstract-full').style.display = 'none'; document.getElementById('2207.03525v1-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> 7 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">arXiv admin note: substantial text overlap with arXiv:1910.00715</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2021 IEEE International Conference on Big Data (Big Data) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1701.02190">arXiv:1701.02190</a> <span> [<a href="https://arxiv.org/pdf/1701.02190">pdf</a>, <a href="https://arxiv.org/ps/1701.02190">ps</a>, <a href="https://arxiv.org/format/1701.02190">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</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/10.1504/IJBIDM.2009.029076">10.1504/IJBIDM.2009.029076 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Fragmenting very large XML data warehouses via K-means clustering algorithm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cuzzocrea%2C+A">Alfredo Cuzzocrea</a>, <a href="/search/cs?searchtype=author&query=Darmont%2C+J">J茅r么me Darmont</a>, <a href="/search/cs?searchtype=author&query=Mahboubi%2C+H">Hadj Mahboubi</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="1701.02190v1-abstract-short" style="display: inline;"> XML data sources are more and more gaining popularity in the context of a wide family of Business Intelligence (BI) and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing semi-structured and complex multidimensional data. As a consequence, many XML data warehouse models have been proposed during past years in order to handle hetero-geneity a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.02190v1-abstract-full').style.display = 'inline'; document.getElementById('1701.02190v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1701.02190v1-abstract-full" style="display: none;"> XML data sources are more and more gaining popularity in the context of a wide family of Business Intelligence (BI) and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing semi-structured and complex multidimensional data. As a consequence, many XML data warehouse models have been proposed during past years in order to handle hetero-geneity and complexity of multidimensional data in a way traditional relational data warehouse approaches fail to achieve. However, XML-native database systems currently suffer from limited performance, both in terms of volumes of manageable data and query response time. Therefore , recent research efforts are focusing the attention on fragmentation techniques, which are able to overcome the limitations above. Derived horizontal fragmentation is already used in relational data warehouses, and can definitely be adapted to the XML context. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses, and, with more emphasis, distributed data warehouse architectures. Inspired by this research challenge, in this paper we propose the use of K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses, and, at the same time, completely controlling and determining the number of originated fragments via adequately setting the parameter K. We complete our analytical contribution by means of a comprehensive experimental assessment where we compare the efficiency of our proposed XML data warehouse fragmentation technique against those of classical derived horizontal fragmentation algorithms adapted to XML data warehouses. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.02190v1-abstract-full').style.display = 'none'; document.getElementById('1701.02190v1-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, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> International Journal of Business Intelligence and Data Mining, Inderscience, 2009, 4 (3/4), pp.301-328 </p> </li> </ol> <div 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