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Machine learning-based systems for malware detection operate in a hostileenvironment. Consequently, adversaries will also target the learning system anduse evasion attacks to bypass the detection of malware. In this paper, weoutline our learning-based system PEberus that got the first place in thedefender challenge of the Microsoft Evasion Competition, resisting a variety ofattacks from independent attackers. Our system combines multiple, diversedefenses: we address the semantic gap, use various classification models, andapply a stateful defense. This competition gives us the unique opportunity toexamine evasion attacks under a realistic scenario. It also highlights thatexisting machine learning methods can be hardened against attacks by thoroughlyanalyzing the attack surface and implementing concepts from adversariallearning. Our defense can serve as an additional baseline in the future tostrengthen the research on secure learning.

Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification