An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection
Younghoon Ban, Myeonghyun Kim, Haehyun Cho*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3535-3563, 2024, DOI:10.32604/cmes.2023.046658
（This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
Abstract Antivirus vendors and the research community employ Machine Learning (ML) or Deep Learning (DL)-based static analysis techniques for efficient identification of new threats, given the continual emergence of novel malware variants. On the other hand, numerous researchers have reported that Adversarial Examples (AEs), generated by manipulating previously detected malware, can successfully evade ML/DL-based classifiers. Commercial antivirus systems, in particular, have been identified as vulnerable to such AEs. This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers. Our attack method utilizes seven different perturbations, including Overlay Append, Section Append, and Break Checksum, capitalizing on the ambiguities present… More >
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