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

1 citations · 1 across the 3 of their papers we have counts for

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3 papers

cs.LG2023

MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection

Aqib Rashid, Jose Such

ML models are known to be vulnerable to adversarial query attacks. In these attacks, queries are iteratively perturbed towards a particular class without any knowledge of the targe…

cs.LG2023★ 1 cited

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

Aqib Rashid, Jose Such

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 att…

cs.LG2022

StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection

Aqib Rashid, Jose Such

Over the years, most research towards defenses against adversarial attacks on machine learning models has been in the image recognition domain. The ML-based malware detection domai…