Statically Detecting Adversarial Malware through Randomised Chaining

Matthew Crawford; Wei Wang; Ruoxi Sun; Minhui Xue

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arXiv · 2111.14037

Statically Detecting Adversarial Malware through Randomised Chaining

Matthew Crawford·Wei Wang·Ruoxi Sun·Minhui Xue

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Abstract

With the rapid growth of malware attacks, more antivirus developers consider deploying machine learning technologies into their productions. Researchers and developers published various machine learning-based detectors with high precision on malware detection in recent years. Although numerous machine learning-based malware detectors are available, they face various machine learning-targeted attacks, including evasion and adversarial attacks. This project explores how and why adversarial examples evade malware detectors, then proposes a randomised chaining method to defend against adversarial malware statically. This research is crucial for working towards combating the pertinent malware cybercrime.

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BibTeX RIS

Matthew Crawford, Wei Wang, Ruoxi Sun, Minhui Xue. 2021-11-28. Statically Detecting Adversarial Malware through Randomised Chaining. https://arxiv.org/abs/2111.14037

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