Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection
This work highlights vulnerabilities in current MTDs for malware detection, showing they are ineffective against practical attacks, which is crucial for security practitioners but incremental in advancing defense strategies.
The study examined the effectiveness of moving target defenses (MTDs) against adversarial attacks in ML-based malware detection, finding that transferability and query attacks achieved high evasion rates across Android and Windows platforms, and that attackers could fingerprint defenses and obtain critical hyperparameters.
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.