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amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth
Mosquera, Alejandro
This paper describes the author's participation in the 3rd edition of the Machine Learning Security Evasion Competition (MLSEC-2021) sponsored by CUJO AI, VM-Ray, MRG-Effitas, Nvidia and Microsoft. As in the previous year the goal was not only developing measures against adversarial attacks on a pre-defined set of malware samples but also finding ways of bypassing other teams' defenses in a simulated cloud environment. The submitted solutions were ranked second in both defender and attacker tracks.
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Indexed in
- Publication date:
- September 28, 2021
- DOI:
-
- Keyword(s):
- Adversarial machine learning Malware detection MLSEC Static malware detection
- License (for files):
- Creative Commons Attribution 4.0 International