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Machine learning-based hardware malware detectors (HMDs) offer a potentialgame changing advantage in defending systems against malware. However, HMDssuffer from adversarial attacks, can be effectively reverse-engineered andsubsequently be evaded, allowing malware to hide from detection. We addressthis issue by proposing a novel HMDs (Stochastic-HMDs) through approximatecomputing, which makes HMDs' inference computation-stochastic, thereby makingHMDs resilient against adversarial evasion attacks. Specifically, we propose toleverage voltage overscaling to induce stochastic computation in the HMDsmodel. We show that such a technique makes HMDs more resilient to bothblack-box adversarial attack scenarios, i.e., reverse-engineering andtransferability. Our experimental results demonstrate that Stochastic-HMDsoffer effective defense against adversarial attacks along with by-product powersavings, without requiring any changes to the hardware/software nor to theHMDs' model, i.e., no retraining or fine tuning is needed. Moreover, based onrecent results in probably approximately correct (PAC) learnability theory, weshow that Stochastic-HMDs are provably more difficult to reverse engineer.

Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling