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behaviour</title> <meta name="description" content="Search results for: malware behaviour"> <meta name="keywords" content="malware behaviour"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="malware behaviour"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1926</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: malware behaviour</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1926</span> Automatic Intelligent Analysis of Malware Behaviour</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hermann%20Dornhackl">Hermann Dornhackl</a>, <a href="https://publications.waset.org/abstracts/search?q=Konstantin%20Kadletz"> Konstantin Kadletz</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Luh"> Robert Luh</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Tavolato"> Paul Tavolato</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we describe the use of formal methods to model malware behaviour. The modelling of harmful behaviour rests upon syntactic structures that represent malicious procedures inside malware. The malicious activities are modelled by a formal grammar, where API calls’ components are the terminals and the set of API calls used in combination to achieve a goal are designated non-terminals. The combination of different non-terminals in various ways and tiers make up the attack vectors that are used by harmful software. Based on these syntactic structures a parser can be generated which takes execution traces as input for pattern recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malware%20behaviour" title="malware behaviour">malware behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=modelling" title=" modelling"> modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=parsing" title=" parsing"> parsing</a>, <a href="https://publications.waset.org/abstracts/search?q=search" title=" search"> search</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20matching" title=" pattern matching"> pattern matching</a> </p> <a href="https://publications.waset.org/abstracts/3774/automatic-intelligent-analysis-of-malware-behaviour" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3774.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1925</span> Survey on Malware Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doaa%20Wael">Doaa Wael</a>, <a href="https://publications.waset.org/abstracts/search?q=Naswa%20Abdelbaky"> Naswa Abdelbaky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malware is malicious software that is built to cause destructive actions and damage information systems and networks. Malware infections increase rapidly, and types of malware have become more sophisticated, which makes the malware detection process more difficult. On the other side, the Internet of Things IoT technology is vulnerable to malware attacks. These IoT devices are always connected to the internet and lack security. This makes them easy for hackers to access. These malware attacks are becoming the go-to attack for hackers. Thus, in order to deal with this challenge, new malware detection techniques are needed. Currently, building a blockchain solution that allows IoT devices to download any file from the internet and to verify/approve whether it is malicious or not is the need of the hour. In recent years, blockchain technology has stood as a solution to everything due to its features like decentralization, persistence, and anonymity. Moreover, using blockchain technology overcomes some difficulties in malware detection and improves the malware detection ratio over-than the techniques that do not utilize blockchain technology. In this paper, we study malware detection models which are based on blockchain technology. Furthermore, we elaborate on the effect of blockchain technology in malware detection, especially in the android environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malware%20analysis" title="malware analysis">malware analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=blockchain" title=" blockchain"> blockchain</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20attacks" title=" malware attacks"> malware attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection%20approaches" title=" malware detection approaches"> malware detection approaches</a> </p> <a href="https://publications.waset.org/abstracts/164823/survey-on-malware-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164823.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">87</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1924</span> User’s Susceptibility Factors to Malware Attacks: A Systematic Literature Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Awad%20A.%20Younis">Awad A. Younis</a>, <a href="https://publications.waset.org/abstracts/search?q=Elise%20Stronberg"> Elise Stronberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Shifa%20Noor"> Shifa Noor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malware attacks due to end-user vulnerabilities have been noticeably increased in the past few years. Investigating the factors that make an end-user vulnerable to those attacks is critical because they can be utilized to set up proactive strategies such as awareness and education to mitigate the impacts of those attacks. Some existing studies investigated demographic, behavioral, and cultural factors that make an end-user susceptible to malware attacks. However, it has been challenging to draw more general conclusions from individual studies due to the varieties in the type of end-users and different types of malware. Therefore, we conducted a systematic literature review (SLR) of the existing research for end-user susceptibility factors to malware attacks. The results showed while some demographic factors are mostly associated with malware infection regardless of the end users' type, age, and gender are not consistent among the same and different types of end-users. Besides, the association of culture and personality factors with malware infection are consistent in most of the selected studies and for all type of end-users. Moreover, malware infection varies based on age, geographic location, and host types. We propose that future studies should carefully take into consideration the type of end-users because different end users may be exposed to different threats or be targeted based on their user domains’ characteristics. Additionally, as different types of malware use different tactics to trick end-users, taking the malware types into consideration is important. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title="cybersecurity">cybersecurity</a>, <a href="https://publications.waset.org/abstracts/search?q=malware" title=" malware"> malware</a>, <a href="https://publications.waset.org/abstracts/search?q=end-users" title=" end-users"> end-users</a>, <a href="https://publications.waset.org/abstracts/search?q=demographics" title=" demographics"> demographics</a>, <a href="https://publications.waset.org/abstracts/search?q=personality" title=" personality"> personality</a>, <a href="https://publications.waset.org/abstracts/search?q=culture" title=" culture"> culture</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20literature%20review" title=" systematic literature review"> systematic literature review</a> </p> <a href="https://publications.waset.org/abstracts/135708/users-susceptibility-factors-to-malware-attacks-a-systematic-literature-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135708.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">230</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1923</span> Using Autoencoder as Feature Extractor for Malware Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umm-E-Hani">Umm-E-Hani</a>, <a href="https://publications.waset.org/abstracts/search?q=Faiza%20Babar"> Faiza Babar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanif%20Durad"> Hanif Durad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malware" title="malware">malware</a>, <a href="https://publications.waset.org/abstracts/search?q=auto%20encoders" title=" auto encoders"> auto encoders</a>, <a href="https://publications.waset.org/abstracts/search?q=automated%20feature%20engineering" title=" automated feature engineering"> automated feature engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/172184/using-autoencoder-as-feature-extractor-for-malware-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172184.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1922</span> A Study of Permission-Based Malware Detection Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ratun%20Rahman">Ratun Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafid%20Islam"> Rafid Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Akin%20Ahmed"> Akin Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamrul%20Hasan"> Kamrul Hasan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Mahmud"> Hasan Mahmud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, and TensorFlow Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. The test set consists of 1,168 samples (among these android applications, 602 are malware and 566 are benign applications), each consisting of 948 features (permissions). Using the permission-based dataset, the machine learning algorithms then produce accuracy rates above 80%, except the Naive Bayes Algorithm with 65% accuracy. Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=android%20malware%20detection" title="android malware detection">android malware detection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=malware" title=" malware"> malware</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20analysis" title=" malware analysis"> malware analysis</a> </p> <a href="https://publications.waset.org/abstracts/150026/a-study-of-permission-based-malware-detection-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150026.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1921</span> Comprehensive Review of Adversarial Machine Learning in PDF Malware</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preston%20Nabors">Preston Nabors</a>, <a href="https://publications.waset.org/abstracts/search?q=Nasseh%20Tabrizi"> Nasseh Tabrizi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portable Document Format (PDF) files have gained significant popularity for sharing and distributing documents due to their universal compatibility. However, the widespread use of PDF files has made them attractive targets for cybercriminals, who exploit vulnerabilities to deliver malware and compromise the security of end-user systems. This paper reviews notable contributions in PDF malware detection, including static, dynamic, signature-based, and hybrid analysis. It presents a comprehensive examination of PDF malware detection techniques, focusing on the emerging threat of adversarial sampling and the need for robust defense mechanisms. The paper highlights the vulnerability of machine learning classifiers to evasion attacks. It explores adversarial sampling techniques in PDF malware detection to produce mimicry and reverse mimicry evasion attacks, which aim to bypass detection systems. Improvements for future research are identified, including accessible methods, applying adversarial sampling techniques to malicious payloads, evaluating other models, evaluating the importance of features to malware, implementing adversarial defense techniques, and conducting comprehensive examination across various scenarios. By addressing these opportunities, researchers can enhance PDF malware detection and develop more resilient defense mechanisms against adversarial attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20attacks" title="adversarial attacks">adversarial attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20defense" title=" adversarial defense"> adversarial defense</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20machine%20learning" title=" adversarial machine learning"> adversarial machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=PDF%20malware" title=" PDF malware"> PDF malware</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection" title=" malware detection"> malware detection</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection%20evasion" title=" malware detection evasion"> malware detection evasion</a> </p> <a href="https://publications.waset.org/abstracts/184556/comprehensive-review-of-adversarial-machine-learning-in-pdf-malware" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184556.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">39</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1920</span> A Machine Learning Approach to Detecting Evasive PDF Malware</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vareesha%20Masood">Vareesha Masood</a>, <a href="https://publications.waset.org/abstracts/search?q=Ammara%20Gul"> Ammara Gul</a>, <a href="https://publications.waset.org/abstracts/search?q=Nabeeha%20Areej"> Nabeeha Areej</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Asif%20Masood"> Muhammad Asif Masood</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamna%20Imran"> Hamna Imran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PDF" title="PDF">PDF</a>, <a href="https://publications.waset.org/abstracts/search?q=PDF%20malware" title=" PDF malware"> PDF malware</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifier" title=" decision tree classifier"> decision tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20classifier" title=" random forest classifier"> random forest classifier</a> </p> <a href="https://publications.waset.org/abstracts/172206/a-machine-learning-approach-to-detecting-evasive-pdf-malware" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172206.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1919</span> Suggestion for Malware Detection Agent Considering Network Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ji-Hoon%20Hong">Ji-Hoon Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong-Hee%20Kim"> Dong-Hee Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Nam-Uk%20Kim"> Nam-Uk Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Tai-Myoung%20Chung"> Tai-Myoung Chung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Smartphone users are increasing rapidly. Accordingly, many companies are running BYOD (Bring Your Own Device: Policies to bring private-smartphones to the company) policy to increase work efficiency. However, smartphones are always under the threat of malware, thus the company network that is connected smartphone is exposed to serious risks. Most smartphone malware detection techniques are to perform an independent detection (perform the detection of a single target application). In this paper, we analyzed a variety of intrusion detection techniques. Based on the results of analysis propose an agent using the network IDS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=android%20malware%20detection" title="android malware detection">android malware detection</a>, <a href="https://publications.waset.org/abstracts/search?q=software-defined%20network" title=" software-defined network"> software-defined network</a>, <a href="https://publications.waset.org/abstracts/search?q=interaction%20environment" title=" interaction environment"> interaction environment</a>, <a href="https://publications.waset.org/abstracts/search?q=android%20malware%20detection" title=" android malware detection"> android malware detection</a>, <a href="https://publications.waset.org/abstracts/search?q=software-defined%20network" title=" software-defined network"> software-defined network</a>, <a href="https://publications.waset.org/abstracts/search?q=interaction%20environment" title=" interaction environment"> interaction environment</a> </p> <a href="https://publications.waset.org/abstracts/39330/suggestion-for-malware-detection-agent-considering-network-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39330.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">433</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1918</span> Malware Beaconing Detection by Mining Large-scale DNS Logs for Targeted Attack Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrii%20Shalaginov">Andrii Shalaginov</a>, <a href="https://publications.waset.org/abstracts/search?q=Katrin%20Franke"> Katrin Franke</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiongwei%20Huang"> Xiongwei Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the leading problems in Cyber Security today is the emergence of targeted attacks conducted by adversaries with access to sophisticated tools. These attacks usually steal senior level employee system privileges, in order to gain unauthorized access to confidential knowledge and valuable intellectual property. Malware used for initial compromise of the systems are sophisticated and may target zero-day vulnerabilities. In this work we utilize common behaviour of malware called ”beacon”, which implies that infected hosts communicate to Command and Control servers at regular intervals that have relatively small time variations. By analysing such beacon activity through passive network monitoring, it is possible to detect potential malware infections. So, we focus on time gaps as indicators of possible C2 activity in targeted enterprise networks. We represent DNS log files as a graph, whose vertices are destination domains and edges are timestamps. Then by using four periodicity detection algorithms for each pair of internal-external communications, we check timestamp sequences to identify the beacon activities. Finally, based on the graph structure, we infer the existence of other infected hosts and malicious domains enrolled in the attack activities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malware%20detection" title="malware detection">malware detection</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20security" title=" network security"> network security</a>, <a href="https://publications.waset.org/abstracts/search?q=targeted%20attack" title=" targeted attack"> targeted attack</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20intelligence" title=" computational intelligence"> computational intelligence</a> </p> <a href="https://publications.waset.org/abstracts/44685/malware-beaconing-detection-by-mining-large-scale-dns-logs-for-targeted-attack-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44685.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">263</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1917</span> Modeling and Stability Analysis of Viral Propagation in Wireless Mesh Networking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haowei%20Chen">Haowei Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaiqi%20Xiong"> Kaiqi Xiong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to answer how malware will propagate in Wireless Mesh Networks (WMNs) and how communication radius and distributed density of nodes affects the process of spreading. The above analysis is essential for devising network-wide strategies to counter malware. We answer these questions by developing an improved dynamical system that models malware propagation in the area where nodes were uniformly distributed. The proposed model captures both the spatial and temporal dynamics regarding the malware spreading process. Equilibrium and stability are also discussed based on the threshold of the system. If the threshold is less than one, the infected nodes disappear, and if the threshold is greater than one, the infected nodes asymptotically stabilize at the endemic equilibrium. Numerical simulations are investigated about communication radius and distributed density of nodes in WMNs, which allows us to draw various insights that can be used to guide security defense. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bluetooth%20security" title="Bluetooth security">Bluetooth security</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20propagation" title=" malware propagation"> malware propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20mesh%20networks" title=" wireless mesh networks"> wireless mesh networks</a>, <a href="https://publications.waset.org/abstracts/search?q=stability%20analysis" title=" stability analysis"> stability analysis</a> </p> <a href="https://publications.waset.org/abstracts/146464/modeling-and-stability-analysis-of-viral-propagation-in-wireless-mesh-networking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146464.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">98</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1916</span> Malware Detection in Mobile Devices by Analyzing Sequences of System Calls</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Maestre%20Vidal">Jorge Maestre Vidal</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Lucila%20Sandoval%20Orozco"> Ana Lucila Sandoval Orozco</a>, <a href="https://publications.waset.org/abstracts/search?q=Luis%20Javier%20Garc%C3%ADa%20Villalba"> Luis Javier García Villalba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increase in popularity of mobile devices, new and varied forms of malware have emerged. Consequently, the organizations for cyberdefense have echoed the need to deploy more effective defensive schemes adapted to the challenges posed by these recent monitoring environments. In order to contribute to their development, this paper presents a malware detection strategy for mobile devices based on sequence alignment algorithms. Unlike the previous proposals, only the system calls performed during the startup of applications are studied. In this way, it is possible to efficiently study in depth, the sequences of system calls executed by the applications just downloaded from app stores, and initialize them in a secure and isolated environment. As demonstrated in the performed experimentation, most of the analyzed malicious activities were successfully identified in their boot processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=android" title="android">android</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20security" title=" information security"> information security</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20systems" title=" intrusion detection systems"> intrusion detection systems</a>, <a href="https://publications.waset.org/abstracts/search?q=malware" title=" malware"> malware</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20devices" title=" mobile devices"> mobile devices</a> </p> <a href="https://publications.waset.org/abstracts/70344/malware-detection-in-mobile-devices-by-analyzing-sequences-of-system-calls" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70344.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">303</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1915</span> Research on Malware Application Patterns of Using Permission Monitoring System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seung-Hwan%20Ju">Seung-Hwan Ju</a>, <a href="https://publications.waset.org/abstracts/search?q=Yo-Han%20Choi"> Yo-Han Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hee-Suk%20Seo"> Hee-Suk Seo</a>, <a href="https://publications.waset.org/abstracts/search?q=Tae-Kyung%20Kim"> Tae-Kyung Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates the permissions requested by Android applications, and the possibility of identifying suspicious applications based only on information presented to the user before an application is downloaded. The pattern analysis is based on a smaller data set consisting of confirmed malicious applications. The method is evaluated based on its ability to recognize malicious potential in the analyzed applications. In this study, we develop a system to monitor that mobile application permission at application update. This study is a service-based malware analysis. It will be based on the mobile security study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malware%20patterns" title="malware patterns">malware patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=application%20permission" title=" application permission"> application permission</a>, <a href="https://publications.waset.org/abstracts/search?q=application%20analysis" title=" application analysis"> application analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a> </p> <a href="https://publications.waset.org/abstracts/19632/research-on-malware-application-patterns-of-using-permission-monitoring-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19632.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">523</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1914</span> Towards an Enhanced Compartmental Model for Profiling Malware Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jessemyn%20Modiini">Jessemyn Modiini</a>, <a href="https://publications.waset.org/abstracts/search?q=Timothy%20Lynar"> Timothy Lynar</a>, <a href="https://publications.waset.org/abstracts/search?q=Elena%20Sitnikova"> Elena Sitnikova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a novel enhanced compartmental model for malware spread analysis in cyber security. This paper applies cyber security data features to epidemiological compartmental models to model the infectious potential of malware. Compartmental models are most efficient for calculating the infectious potential of a disease. In this paper, we discuss and profile epidemiologically relevant data features from a Domain Name System (DNS) dataset. We then apply these features to epidemiological compartmental models to network traffic features. This paper demonstrates how epidemiological principles can be applied to the novel analysis of key cybersecurity behaviours and trends and provides insight into threat modelling above that of kill-chain analysis. In applying deterministic compartmental models to a cyber security use case, the authors analyse the deficiencies and provide an enhanced stochastic model for cyber epidemiology. This enhanced compartmental model (SUEICRN model) is contrasted with the traditional SEIR model to demonstrate its efficacy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title="cybersecurity">cybersecurity</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemiology" title=" epidemiology"> epidemiology</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber%20epidemiology" title=" cyber epidemiology"> cyber epidemiology</a>, <a href="https://publications.waset.org/abstracts/search?q=malware" title=" malware"> malware</a> </p> <a href="https://publications.waset.org/abstracts/152584/towards-an-enhanced-compartmental-model-for-profiling-malware-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152584.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">107</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1913</span> A Genetic Algorithm Based Ensemble Method with Pairwise Consensus Score on Malware Cacophonous Labels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shih-Yu%20Wang">Shih-Yu Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Shun-Wen%20Hsiao"> Shun-Wen Hsiao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the field of cybersecurity, there exists many vendors giving malware samples classified results, namely naming after the label that contains some important information which is also called AV label. Lots of researchers relay on AV labels for research. Unfortunately, AV labels are too cluttered. They do not have a fixed format and fixed naming rules because the naming results were based on each classifiers' viewpoints. A way to fix the problem is taking a majority vote. However, voting can sometimes create problems of bias. Thus, we create a novel ensemble approach which does not rely on the cacophonous naming result but depend on group identification to aggregate everyone's opinion. To achieve this purpose, we develop an scoring system called Pairwise Consensus Score (PCS) to calculate result similarity. The entire method architecture combine Genetic Algorithm and PCS to find maximum consensus in the group. Experimental results revealed that our method outperformed the majority voting by 10% in term of the score. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20learning" title=" ensemble learning"> ensemble learning</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20family" title=" malware family"> malware family</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20labeling" title=" malware labeling"> malware labeling</a>, <a href="https://publications.waset.org/abstracts/search?q=AV%20labels" title=" AV labels"> AV labels</a> </p> <a href="https://publications.waset.org/abstracts/159376/a-genetic-algorithm-based-ensemble-method-with-pairwise-consensus-score-on-malware-cacophonous-labels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159376.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1912</span> Application of Federated Learning in the Health Care Sector for Malware Detection and Mitigation Using Software-Defined Networking Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Dinelka%20Panagoda">A. Dinelka Panagoda</a>, <a href="https://publications.waset.org/abstracts/search?q=Bathiya%20Bandara"> Bathiya Bandara</a>, <a href="https://publications.waset.org/abstracts/search?q=Chamod%20Wijetunga"> Chamod Wijetunga</a>, <a href="https://publications.waset.org/abstracts/search?q=Chathura%20Malinda"> Chathura Malinda</a>, <a href="https://publications.waset.org/abstracts/search?q=Lakmal%20Rupasinghe"> Lakmal Rupasinghe</a>, <a href="https://publications.waset.org/abstracts/search?q=Chethana%20Liyanapathirana"> Chethana Liyanapathirana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software-defined%20network" title="software-defined network">software-defined network</a>, <a href="https://publications.waset.org/abstracts/search?q=federated%20learning" title=" federated learning"> federated learning</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy" title=" privacy"> privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20clinical%20environment" title=" integrated clinical environment"> integrated clinical environment</a>, <a href="https://publications.waset.org/abstracts/search?q=decentralized%20learning" title=" decentralized learning"> decentralized learning</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection" title=" malware detection"> malware detection</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20mitigation" title=" malware mitigation"> malware mitigation</a> </p> <a href="https://publications.waset.org/abstracts/149784/application-of-federated-learning-in-the-health-care-sector-for-malware-detection-and-mitigation-using-software-defined-networking-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149784.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1911</span> A Comparative Study of Virus Detection Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sulaiman%20Al%20amro">Sulaiman Al amro</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Alkhalifah"> Ali Alkhalifah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The growing number of computer viruses and the detection of zero day malware have been the concern for security researchers for a large period of time. Existing antivirus products (AVs) rely on detecting virus signatures which do not provide a full solution to the problems associated with these viruses. The use of logic formulae to model the behaviour of viruses is one of the most encouraging recent developments in virus research, which provides alternatives to classic virus detection methods. In this paper, we proposed a comparative study about different virus detection techniques. This paper provides the advantages and drawbacks of different detection techniques. Different techniques will be used in this paper to provide a discussion about what technique is more effective to detect computer viruses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20viruses" title="computer viruses">computer viruses</a>, <a href="https://publications.waset.org/abstracts/search?q=virus%20detection" title=" virus detection"> virus detection</a>, <a href="https://publications.waset.org/abstracts/search?q=signature-based" title=" signature-based"> signature-based</a>, <a href="https://publications.waset.org/abstracts/search?q=behaviour-based" title=" behaviour-based"> behaviour-based</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic-based" title=" heuristic-based "> heuristic-based </a> </p> <a href="https://publications.waset.org/abstracts/28688/a-comparative-study-of-virus-detection-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28688.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">484</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1910</span> A Static Android Malware Detection Based on Actual Used Permissions Combination and API Calls</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoqing%20Wang">Xiaoqing Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Junfeng%20Wang"> Junfeng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaolan%20Zhu"> Xiaolan Zhu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Android operating system has been recognized by most application developers because of its good open-source and compatibility, which enriches the categories of applications greatly. However, it has become the target of malware attackers due to the lack of strict security supervision mechanisms, which leads to the rapid growth of malware, thus bringing serious safety hazards to users. Therefore, it is critical to detect Android malware effectively. Generally, the permissions declared in the AndroidManifest.xml can reflect the function and behavior of the application to a large extent. Since current Android system has not any restrictions to the number of permissions that an application can request, developers tend to apply more than actually needed permissions in order to ensure the successful running of the application, which results in the abuse of permissions. However, some traditional detection methods only consider the requested permissions and ignore whether it is actually used, which leads to incorrect identification of some malwares. Therefore, a machine learning detection method based on the actually used permissions combination and API calls was put forward in this paper. Meanwhile, several experiments are conducted to evaluate our methodology. The result shows that it can detect unknown malware effectively with higher true positive rate and accuracy while maintaining a low false positive rate. Consequently, the AdaboostM1 (J48) classification algorithm based on information gain feature selection algorithm has the best detection result, which can achieve an accuracy of 99.8%, a true positive rate of 99.6% and a lowest false positive rate of 0. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=android" title="android">android</a>, <a href="https://publications.waset.org/abstracts/search?q=API%20Calls" title=" API Calls"> API Calls</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=permissions%20combination" title=" permissions combination"> permissions combination</a> </p> <a href="https://publications.waset.org/abstracts/51217/a-static-android-malware-detection-based-on-actual-used-permissions-combination-and-api-calls" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51217.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">329</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1909</span> Fusion Models for Cyber Threat Defense: Integrating Clustering, Random Forests, and Support Vector Machines to Against Windows Malware</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azita%20Ramezani">Azita Ramezani</a>, <a href="https://publications.waset.org/abstracts/search?q=Atousa%20Ramezani"> Atousa Ramezani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the ever-escalating landscape of windows malware the necessity for pioneering defense strategies turns into undeniable this study introduces an avant-garde approach fusing the capabilities of clustering random forests and support vector machines SVM to combat the intricate web of cyber threats our fusion model triumphs with a staggering accuracy of 98.67 and an equally formidable f1 score of 98.68 a testament to its effectiveness in the realm of windows malware defense by deciphering the intricate patterns within malicious code our model not only raises the bar for detection precision but also redefines the paradigm of cybersecurity preparedness this breakthrough underscores the potential embedded in the fusion of diverse analytical methodologies and signals a paradigm shift in fortifying against the relentless evolution of windows malicious threats as we traverse through the dynamic cybersecurity terrain this research serves as a beacon illuminating the path toward a resilient future where innovative fusion models stand at the forefront of cyber threat defense. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fusion%20models" title="fusion models">fusion models</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber%20threat%20defense" title=" cyber threat defense"> cyber threat defense</a>, <a href="https://publications.waset.org/abstracts/search?q=windows%20malware" title=" windows malware"> windows malware</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forests" title=" random forests"> random forests</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines%20%28SVM%29" title=" support vector machines (SVM)"> support vector machines (SVM)</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=f1-score" title=" f1-score"> f1-score</a>, <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title=" cybersecurity"> cybersecurity</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20code%20detection" title=" malicious code detection"> malicious code detection</a> </p> <a href="https://publications.waset.org/abstracts/179650/fusion-models-for-cyber-threat-defense-integrating-clustering-random-forests-and-support-vector-machines-to-against-windows-malware" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179650.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1908</span> A Comparative Study of Malware Detection Techniques Using Machine Learning Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cristina%20Vatamanu">Cristina Vatamanu</a>, <a href="https://publications.waset.org/abstracts/search?q=Doina%20Cosovan"> Doina Cosovan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dragos%20Gavrilut"> Dragos Gavrilut</a>, <a href="https://publications.waset.org/abstracts/search?q=Henri%20Luchian"> Henri Luchian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ensembles" title="ensembles">ensembles</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20positives" title=" false positives"> false positives</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=one%20side%20class%20algorithm" title=" one side class algorithm"> one side class algorithm</a> </p> <a href="https://publications.waset.org/abstracts/30093/a-comparative-study-of-malware-detection-techniques-using-machine-learning-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30093.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">292</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1907</span> Literature Review: Adversarial Machine Learning Defense in Malware Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leidy%20M.%20Aldana">Leidy M. Aldana</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20E.%20Camargo"> Jorge E. Camargo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malware" title="Malware">Malware</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial" title=" adversarial"> adversarial</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=defense" title=" defense"> defense</a>, <a href="https://publications.waset.org/abstracts/search?q=attack" title=" attack"> attack</a> </p> <a href="https://publications.waset.org/abstracts/177946/literature-review-adversarial-machine-learning-defense-in-malware-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177946.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">63</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1906</span> Survey Based Data Security Evaluation in Pakistan Financial Institutions against Malicious Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ghani">Naveed Ghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Samreen%20Javed"> Samreen Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20worms" title="network worms">network worms</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20infection%20propagating%20malicious%20code" title=" malware infection propagating malicious code"> malware infection propagating malicious code</a>, <a href="https://publications.waset.org/abstracts/search?q=virus" title=" virus"> virus</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=VPN" title=" VPN"> VPN</a> </p> <a href="https://publications.waset.org/abstracts/2550/survey-based-data-security-evaluation-in-pakistan-financial-institutions-against-malicious-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2550.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">358</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1905</span> Study on Security and Privacy Issues of Mobile Operating Systems Based on Malware Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huang%20Dennis">Huang Dennis</a>, <a href="https://publications.waset.org/abstracts/search?q=Aurelio%20Aziel"> Aurelio Aziel</a>, <a href="https://publications.waset.org/abstracts/search?q=Burra%20Venkata%20Durga%20Kumar"> Burra Venkata Durga Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, smartphones and mobile operating systems have been popularly widespread in our daily lives. As people use smartphones, they tend to store more private and essential data on their devices, because of this it is very important to develop more secure mobile operating systems and cloud storage to secure the data. However, several factors can cause security risks in mobile operating systems such as malware, malicious app, phishing attacks, ransomware, and more, all of which can cause a big problem for users as they can access the user's private data. Those problems can cause data loss, financial loss, identity theft, and other serious consequences. Other than that, during the pandemic, people will use their mobile devices more and do all sorts of transactions online, which may lead to more victims of online scams and inexperienced users being the target. With the increase in attacks, researchers have been actively working to develop several countermeasures to enhance the security of operating systems. This study aims to provide an overview of the security and privacy issues in mobile operating systems, identifying the potential risk of operating systems, and the possible solutions. By examining these issues, we want to provide an easy understanding to users and researchers to improve knowledge and develop more secure mobile operating systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20operating%20system" title="mobile operating system">mobile operating system</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy" title=" privacy"> privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=Malware" title=" Malware"> Malware</a> </p> <a href="https://publications.waset.org/abstracts/168876/study-on-security-and-privacy-issues-of-mobile-operating-systems-based-on-malware-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168876.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1904</span> Shopping Behaviour of Ethnic Groups in Indian Culture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hari%20Govindmishra">Hari Govindmishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarabjot%20Singh"> Sarabjot Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study offers an approach to understand different determinants of shopping behaviour, and the effect of ethnicity on shopping behaviour. The results reveal that the Indian culture is composite in nature and because of which there is no difference between different ethnic groups in their preference for three shopping behaviour determinants, viz., status consciousness, need for touch and companion opinion. The research model investigates the relevant relationship between these constructs by using a structural equation modelling approach, which reveals that status consciousness, need for touch and companion opinion are significant determinants of shopping behaviour. Consequently, the shopping behaviour managers have to understand the collective nature of Indian ethnic consumers in their shopping behaviour. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ethnic%20groups" title="ethnic groups">ethnic groups</a>, <a href="https://publications.waset.org/abstracts/search?q=status%20consciousness" title=" status consciousness"> status consciousness</a>, <a href="https://publications.waset.org/abstracts/search?q=companion%20opinion" title=" companion opinion"> companion opinion</a>, <a href="https://publications.waset.org/abstracts/search?q=need%20for%20touch" title=" need for touch"> need for touch</a>, <a href="https://publications.waset.org/abstracts/search?q=shopping%20behaviour" title=" shopping behaviour"> shopping behaviour</a> </p> <a href="https://publications.waset.org/abstracts/42476/shopping-behaviour-of-ethnic-groups-in-indian-culture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42476.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">451</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1903</span> Personal Characteristics Related to Hasty Behaviour in Korea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sun%20Jin%20Park">Sun Jin Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyung-Ja%20Cho"> Kyung-Ja Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study focused on characteristics related to hasty behaviour. To investigate the relation between personal characteristics and hasty behaviour, 601 data were collected, 335 males and 256 females answered their own 'social avoidance and distress’, ‘anxiety’, ‘sensation seeking', 'hope', and ' hasty behaviour. And then 591 data were used for the analysis. The factor analysis resulted hasty behaviour consisted of 5 factors, time pressure, isolation, uncomfortable situation, boring condition, and expectation of reward. The result showed anxiety, sensation seeking, and hope related to hasty behaviour. Specifically, anxiety was involved in every hasty behaviour. This result means that psychological tension and worry are related to hasty behaviour in common. 'Social avoidance and distress', 'sensation seeking' and 'hope' influenced on hasty behaviour under time pressure, in isolation, in expectation of rewards respectively. This means that each factor of hasty behaviour has anxiety as its basis, expressed through a varied nature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hasty%20behaviour" title="hasty behaviour">hasty behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20avoidance%20and%20distress" title=" social avoidance and distress"> social avoidance and distress</a>, <a href="https://publications.waset.org/abstracts/search?q=anxiety" title=" anxiety"> anxiety</a>, <a href="https://publications.waset.org/abstracts/search?q=sensation%20seeking" title=" sensation seeking"> sensation seeking</a>, <a href="https://publications.waset.org/abstracts/search?q=hope" title=" hope"> hope</a> </p> <a href="https://publications.waset.org/abstracts/5484/personal-characteristics-related-to-hasty-behaviour-in-korea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5484.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">328</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1902</span> The Impact of Living at Home during the COVID-19 on Young Children’s Disruptive Behaviours</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhou%20Yuwei">Zhou Yuwei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study used the multidimensional rating scale for disruptive behaviour in preschool children (parent version) to assess changes in the disruptive behaviour (tantrums, disobedience, aggression, and low level of concern for others) of 200 young children in Nanjing, Jiangsu Province, China, before and after living at home during the new crown epidemic, and five additional teachers of young children were selected to conduct interviews on the performance and changes in their disruptive behaviour at school. The following conclusions were drawn from the questionnaires and interviews: (1) 49% of the children showed a decrease in disruptive behaviour compared to the pre-epidemic period; (2) boys were more disruptive than girls due to individual factors; (3) children with a decrease in disruptive behaviour were more likely to have democratic and authoritative parenting styles due to parental education and upbringing; and the higher the level of parental education, the greater the decrease in disruptive behaviour. (4) For parents who worked outside the home during the epidemic and who did not work, disruptive behaviour scores were higher for their children. Meanwhile, disruptive behaviour was more pronounced the longer the child used electronic devices. The longer the parent-child interaction, the less disruptive behaviour was evident. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disruptive%20behaviour" title="disruptive behaviour">disruptive behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=home%20life" title=" home life"> home life</a>, <a href="https://publications.waset.org/abstracts/search?q=children" title=" children"> children</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a> </p> <a href="https://publications.waset.org/abstracts/155607/the-impact-of-living-at-home-during-the-covid-19-on-young-childrens-disruptive-behaviours" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155607.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1901</span> Strategic Workplace Security: The Role of Malware and the Threat of Internal Vulnerability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Modesta%20E.%20Ezema">Modesta E. Ezema</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20C.%20Ezema"> Christopher C. Ezema</a>, <a href="https://publications.waset.org/abstracts/search?q=Christian%20C.%20Ugwu"> Christian C. Ugwu</a>, <a href="https://publications.waset.org/abstracts/search?q=Udoka%20F.%20Eze"> Udoka F. Eze</a>, <a href="https://publications.waset.org/abstracts/search?q=Florence%20M.%20Babalola"> Florence M. Babalola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Some employees knowingly or unknowingly contribute to loss of data and also expose data to threat in the process of getting their jobs done. Many organizations today are faced with the challenges of how to secure their data as cyber criminals constantly devise new ways of attacking the organization’s secret data. However, this paper enlists the latest strategies that must be put in place in order to protect these important data from being attacked in a collaborative work place. It also introduces us to Advanced Persistent Threats (APTs) and how it works. The empirical study was conducted to collect data from the employee in data centers on how data could be protected from malicious codes and cyber criminals and their responses are highly considered to help checkmate the activities of malicious code and cyber criminals in our work places. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data" title="data">data</a>, <a href="https://publications.waset.org/abstracts/search?q=employee" title=" employee"> employee</a>, <a href="https://publications.waset.org/abstracts/search?q=malware" title=" malware"> malware</a>, <a href="https://publications.waset.org/abstracts/search?q=work%20place" title=" work place"> work place</a> </p> <a href="https://publications.waset.org/abstracts/61588/strategic-workplace-security-the-role-of-malware-and-the-threat-of-internal-vulnerability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61588.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1900</span> Behaviour of Rc Column under Biaxial Cyclic Loading-State of the Art</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Pavithra">L. Pavithra</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Sharmila"> R. Sharmila</a>, <a href="https://publications.waset.org/abstracts/search?q=Shivani%20Sridhar"> Shivani Sridhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Columns severe structural damage needs proportioning a significant portion of earthquake energy can be dissipated yielding in the beams. Presence of axial load along with cyclic loading has a significant influence on column. The objective of this paper is to present the analytical results of columns subjected to biaxial cyclic loading. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RC%20column" title="RC column">RC column</a>, <a href="https://publications.waset.org/abstracts/search?q=Seismic%20behaviour" title=" Seismic behaviour"> Seismic behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20behaviour" title=" cyclic behaviour"> cyclic behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=biaxial%20testing" title=" biaxial testing"> biaxial testing</a>, <a href="https://publications.waset.org/abstracts/search?q=ductile%20behaviour" title=" ductile behaviour"> ductile behaviour</a> </p> <a href="https://publications.waset.org/abstracts/26015/behaviour-of-rc-column-under-biaxial-cyclic-loading-state-of-the-art" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26015.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1899</span> Enhancing Email Security: A Multi-Layered Defense Strategy Approach and an AI-Powered Model for Identifying and Mitigating Phishing Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anastasios%20Papathanasiou">Anastasios Papathanasiou</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20Liontos"> George Liontos</a>, <a href="https://publications.waset.org/abstracts/search?q=Athanasios%20Katsouras"> Athanasios Katsouras</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasiliki%20Liagkou"> Vasiliki Liagkou</a>, <a href="https://publications.waset.org/abstracts/search?q=Euripides%20Glavas"> Euripides Glavas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=email%20security" title="email security">email security</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=header%20analysis" title=" header analysis"> header analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=threat%20detection" title=" threat detection"> threat detection</a>, <a href="https://publications.waset.org/abstracts/search?q=phishing" title=" phishing"> phishing</a>, <a href="https://publications.waset.org/abstracts/search?q=DMARC" title=" DMARC"> DMARC</a>, <a href="https://publications.waset.org/abstracts/search?q=DKIM" title=" DKIM"> DKIM</a>, <a href="https://publications.waset.org/abstracts/search?q=SPF" title=" SPF"> SPF</a>, <a href="https://publications.waset.org/abstracts/search?q=ai%20model" title=" ai model"> ai model</a> </p> <a href="https://publications.waset.org/abstracts/185525/enhancing-email-security-a-multi-layered-defense-strategy-approach-and-an-ai-powered-model-for-identifying-and-mitigating-phishing-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">59</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1898</span> Using Social Network Analysis for Cyber Threat Intelligence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vasileios%20Anastopoulos">Vasileios Anastopoulos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cyber threat intelligence assists organizations in understanding the threats they face and helps them make educated decisions on preparing their defenses. Sharing of threat intelligence and threat information is increasingly leveraged by organizations and enterprises, and various software solutions are already available, with the open-source malware information sharing platform (MISP) being a popular one. In this work, a methodology for the production of cyber threat intelligence using the threat information stored in MISP is proposed. The methodology leverages the discipline of social network analysis and the diamond model, a model used for intrusion analysis, to produce cyber threat intelligence. The workings are demonstrated with a case study on a production MISP instance of a real organization. The paper concluded with a discussion on the proposed methodology and possible directions for further research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyber%20threat%20intelligence" title="cyber threat intelligence">cyber threat intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=diamond%20model" title=" diamond model"> diamond model</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20information%20sharing%20platform" title=" malware information sharing platform"> malware information sharing platform</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a> </p> <a href="https://publications.waset.org/abstracts/149417/using-social-network-analysis-for-cyber-threat-intelligence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149417.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">178</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1897</span> Ethical Leadership and Employee Creative Behaviour: A Case Study of a State-Owned Enterprise in South Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krishna%20Kistan%20Govender">Krishna Kistan Govender</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20Masianoga"> Alex Masianoga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this explanatory study was to critically understand how ethical leadership impacts employee creative behaviour, as well as the creative behaviour dimensions, in a South African transport and logistics SOE. A quantitative study was conducted using a pre-developed questionnaire, and data for 160 middle and executive managers was analysed through structural equation modelling and multiple regression techniques conducted with the Smart PLS statistical software. All five hypothesized relationships were supported, and it was confirmed that ethical leadership has a significant positive influence on employee creative behaviour, as well as on each of the creative behaviour dimensions, namely: idea exploration, idea generation, idea championing, and idea implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ethical%20leaders" title="ethical leaders">ethical leaders</a>, <a href="https://publications.waset.org/abstracts/search?q=employee%20creative%20behaviour" title=" employee creative behaviour"> employee creative behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=state-owned%20enterprises" title=" state-owned enterprises"> state-owned enterprises</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Africa" title=" South Africa"> South Africa</a> </p> <a href="https://publications.waset.org/abstracts/158053/ethical-leadership-and-employee-creative-behaviour-a-case-study-of-a-state-owned-enterprise-in-south-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158053.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">126</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=malware%20behaviour&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=malware%20behaviour&page=3">3</a></li> <li class="page-item"><a class="page-link" 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