Visualization and deep-learning-based malware variant detection using OpCode-level features

Journal article


Darem, A., Abawajy, J., Makkar, A., Alhashmi, A. and Alanazi, S. 2021. Visualization and deep-learning-based malware variant detection using OpCode-level features. Future Generation Computer Systems. Vol 125 (Dec 2021), pp. 314-323. https://doi.org/10.1016/j.future.2021.06.032
AuthorsDarem, A., Abawajy, J., Makkar, A., Alhashmi, A. and Alanazi, S.
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.

KeywordsDeep learning; Feature engineering; Image visualization; Malware detection; Obfuscated malware; OpCode features
Year2021
JournalFuture Generation Computer Systems
Journal citationVol 125 (Dec 2021), pp. 314-323
PublisherElseiver
ISSN0167-739X
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 statusPublished
Publication dates
Online29 Jun 2021
Dec 2021
Publication process dates
Accepted19 Jun 2021
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https://repository.derby.ac.uk/item/9yx5z/visualization-and-deep-learning-based-malware-variant-detection-using-opcode-level-features

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