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amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth

Name: amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth
Creator: Mosquera, Alejandro
Keywords: Malware detection, MLSEC, Adversarial machine learning, Static malware detection

descriptionPublicationkeyboard_double_arrow_right Report , Other literature type 28 Sep 2021 English Publisher:Zenodo

Authors: Mosquera, Alejandro;

doi: 10.5281/zenodo.5534782 , 10.5281/zenodo.5534783

amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth

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Abstract

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.

Keywords

Malware detection, MLSEC, Adversarial machine learning, Static malware detection

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