mlsanityData to 7 Oct 2026

Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection

arXiv 2302.00537 ↗First posted 1 Feb 2023cs.LG cs.CRcited by 0

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Abstract (arXiv)

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 achieve high levels of evasion against these defenses through existing and novel attack strategies across Android and Windows. We also show that fingerprinting and reconnaissance are possible and demonstrate how attackers may obtain critical defense hyperparameters as well as information about how predictions are produced. Based on our findings, we present key recommendations for future work on the development of effective MTDs for adversarial attacks in ML-based malware detection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 53 of its 116 references that are in the map. First posted on arXiv 1 Feb 2023.

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References · 53

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