Statically Detecting Adversarial Malware through Randomised Chaining
This addresses the issue of adversarial attacks on malware detectors for antivirus developers, but it appears incremental as it builds on existing defense strategies.
The paper tackled the problem of adversarial examples evading machine learning-based malware detectors by proposing a randomised chaining method for static defense, resulting in a method to combat malware cybercrime.
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.