Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus
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2018-10-18
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William Fleshman, Edward Raff, Richard Zak, Mark McLean, Charles Nicholas, Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus, Proceedings of the AAAI Fall 2018 Symposium on Adversary-Aware Learning Techniques and Trends in Cybersecurity, 2018, http://ceur-ws.org/Vol-2269/FSS-18_paper_11.pdf
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Abstract
As machine-learning (ML) based systems for malware detection
become more prevalent, it becomes necessary to quantify
the benefits compared to the more traditional anti-virus
(AV) systems widely used today. It is not practical to build an
agreed upon test set to benchmark malware detection systems
on pure classification performance. Instead we tackle the problem
by creating a new testing methodology, where we evaluate
the change in performance on a set of known benign & malicious
files as adversarial modifications are performed. The
change in performance combined with the evasion techniques
then quantifies a system’s robustness against that approach.
Through these experiments we are able to show in a quantifiable
way how purely ML based systems can be more robust
than AV products at detecting malware that attempts evasion
through modification, but may be slower to adapt in the face
of significantly novel attacks.