Visualization and deep-learning-based malware variant detection using OpCode-level features
Journal article
Authors | Darem, A., Abawajy, J., Makkar, A., Alhashmi, A. and Alanazi, S. |
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Abstract | Malicious software (malware) is a major threat to the systems and networks’ security. Although anti-malware products are used to protect systems and networks against malware attacks, obfuscated malware that is capable of evading analysis and detection by anti-malware software have become prevalent. Therefore, how to detect and remove obfuscated malware from the systems has become a major concern. In this research work, we propose a semi-supervised approach that integrates deep learning, feature engineering, image transformation and processing techniques for obfuscated malware detection. We validated the proposed approach through experiments and compared it with existing approaches. With 99.12% accuracy in detecting obfuscated malware detection, the proposed approach substantially outperformed the other approaches. |
Keywords | Deep learning; Feature engineering; Image visualization; Malware detection; Obfuscated malware; OpCode features |
Year | 2021 |
Journal | Future Generation Computer Systems |
Journal citation | Vol 125 (Dec 2021), pp. 314-323 |
Publisher | Elseiver |
ISSN | 0167-739X |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.future.2021.06.032 |
Web address (URL) | http://www.scopus.com/inward/record.url?eid=2-s2.0-85109080805&partnerID=MN8TOARS |
Output status | Published |
Publication dates | |
Online | 29 Jun 2021 |
Dec 2021 | |
Publication process dates | |
Accepted | 19 Jun 2021 |
https://repository.derby.ac.uk/item/9yx5z/visualization-and-deep-learning-based-malware-variant-detection-using-opcode-level-features
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