Results 71 to 80 of about 2,609 (164)
Efficient malware detection using NLP and deep learning model
Alexandria Engineering JournalMalware has emerged as a significant challenge in contemporary society, growing in tandem with technological advancements. Consequently, the classification of malware has become a pressing concern for various services.
Umesh Gupta +6 more
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ArrayAs one of the major threats in cybersecurity, malware has been growing continuously and steadily. In recent years, researchers have proposed a number of graph representation learning based malware detection methods by leveraging the intrinsic topological
Ruisheng Li, Qilong Zhang, Huimin Shen
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Effective and Reliable Malware Group Classification for a Massive Malware Environment
International Journal of Distributed Sensor Networks, 2016 Most of the cyber-attacks are caused by malware, and damage from them has escalated from cyber space to home appliances and infrastructure, thus affecting the daily living of the people. As such, anticipative analysis and countermeasures for malware have
Taejin Lee, Jin Kwak
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An empirical study of problems and evaluation of IoT malware classification label sources
Journal of King Saud University: Computer and Information SciencesWith the proliferation of malware on IoT devices, research on IoT malicious code has also become more mature. Most studies use learning models to detect or classify malware.
Tianwei Lei +4 more
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Image-based Malware Classification: A Space Filling Curve Approach
2019 IEEE Symposium on Visualization for Cyber Security (VizSec), 2019 Anti-virus (AV) software is effective at distinguishing between benign and malicious programs yet lack the ability to effectively classify malware into their respective family classes. AV vendors receive considerably large volumes of malicious programs daily and so classification is crucial to quickly identify variants of existing malware that would ...
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Exploring Timeline-Based Malware Classification [PDF]
, 2013 Over the decades or so, Anti-Malware (AM) communities have been faced with a substantial increase in malware activity, including the development of ever-more-sophisticated methods of evading detection. Researchers have argued that an AM strategy which is successful in a given time period cannot work at a much later date due to the changes in malware ...
Islam, Rafiqul +2 more
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LDAM: A lightweight dual attention module for optimizing automotive malware classification
ArrayIn recent years, electric vehicles have become prime targets for cyberattacks, with attackers exploiting public charging stations, USB ports, and other entry points to implant malware. This can lead to network outages and power disruptions.
Jiahui Chen, Mingrui Wu, Huiwu Huang
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Generating Synthetic Malware Samples Using Generative AI
IEEE AccessMalware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have increasingly turned to machine learning techniques to combat sophisticated obfuscation methods used in ...
Tiffany Bao +4 more
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Quantum Machine Learning for Malware Classification
In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never been seen. We investigate the possible benefits of quantum algorithms for classification tasks.
Barrué, Grégoire, Quertier, Tony
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An ensemble approach for imbalanced multiclass malware classification using 1D-CNN. [PDF]
PeerJ Comput Sci, 2023 Panda B, Bisoyi SS, Panigrahy S.
europepmc +1 more source