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Visualization and deep-learning-based malware variant detection using OpCode-level features
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DOI：10.1016/j.future.2021.06.032.png)
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Abstract
En 中文
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. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Malware detection
Image visualization
Deep learning
OpCode features
Feature engineering
Obfuscated malware
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Image-Based malware classification using ensemble of CNN architectures (IMCEC).png)
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