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As machine-learning (ML) based systems for malware detection become moreprevalent, it becomes necessary to quantify the benefits compared to the moretraditional anti-virus (AV) systems widely used today. It is not practical tobuild an agreed upon test set to benchmark malware detection systems on pureclassification performance. Instead we tackle the problem by creating a newtesting methodology, where we evaluate the change in performance on a set ofknown benign & malicious files as adversarial modifications are performed. Thechange in performance combined with the evasion techniques then quantifies asystem's robustness against that approach. Through these experiments we areable to show in a quantifiable way how purely ML based systems can be morerobust than AV products at detecting malware that attempts evasion throughmodification, but may be slower to adapt in the face of significantly novelattacks.

Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus