Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection. (arXiv:2302.00537v1 [cs.LG] CROSS LISTED)

Several moving target defenses (MTDs) to counter adversarial ML attacks have
been proposed in recent years. MTDs claim to increase the difficulty for the
attacker in conducting attacks by regularly changing certain elements of the
defense, such as cycling through configurations. To examine these claims, we
study for the first time the effectiveness of several recent MTDs for
adversarial ML attacks applied to the malware detection domain. Under different
threat models, we show that transferability and query attack strategies can …

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