ROOTs 2020: No Need to Teach New Tricks to Old Malware: Winning an Evasion Challenge with XOR-based Adversarial – Fabrício Ceschin

Sanna/ November 12, 2020/ ROOTS/ 0 comments

Adversarial machine learning is so popular nowadays that Machine Learning (ML) based security solutions became the target of many attacks and, as a consequence, they need to adapt to them to be effective. In our talk, we explore attacks in different ML-models used to detect malware, as part of our experience in the Machine Learning Security Evasion Competition (MLSEC) 2020, sponsored by Microsoft and CUJO AI’s Vulnerability Research Lab, in which we managed to finish in first and second positions in the attacker’ and defender challenge, respectively. During the contest’s first edition (2019), participating teams were challenged to bypass three ML models in a white box manner. Our team bypassed all three of them and reported interesting insights about the models’ weaknesses. This year, the challenge evolved into an attack-and-defense model: the teams should either propose

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