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

Darem, A, Abawajy, Jemal, 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, pp. 314-323, doi: 10.1016/j.future.2021.06.032.

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Title Visualization and deep-learning-based malware variant detection using OpCode-level features
Author(s) Darem, A
Abawajy, JemalORCID iD for Abawajy, Jemal orcid.org/0000-0001-8962-1222
Makkar, A
Alhashmi, A
Alanazi, S
Journal name Future Generation Computer Systems
Volume number 125
Start page 314
End page 323
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2021-12
ISSN 0167-739X
Keyword(s) Malware detection
Image visualization
Deep learning
OpCode features
Feature engineering
Obfuscated malware
Summary 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.
Language eng
DOI 10.1016/j.future.2021.06.032
Field of Research 0803 Computer Software
0805 Distributed Computing
0806 Information Systems
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30153799

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