Stochastic-HMDs: Adversarial-Resilient Hardware Malware Detectors via Undervolting

Machine learning-based hardware malware detectors (HMDs) offer a potential game changing advantage in defending systems against malware. However, HMDs suffer from adversarial attacks, can be effectively reverse-engineered and subsequently be evaded, allowing malware to hide from detection. We address this issue by proposing novel HMDs (Stochastic-HMDs), which leverage approximate computing (AC) to harden HMDs against adversarial evasion attacks. Stochastic-HMDs introduce stochastic noise into the computations within the model to build an efficient and low-cost moving-target defense. Specifically, we use controlled undervolting, i.e., scaling the supply voltage below nominal level, to deliberately induce stochastic timing violations in the HMDs’ computations during inference (detection). We show that such technique makes HMDs more resilient to adversarial attacks, especially to reverse-engineering and transferability. Our thorough empirical results substantiate that Stochastic-HMDs offer effective defense against adversarial attacks along with by-product power savings, without requiring any changes to the hardware/software nor to the HMDs’ model, i.e., no retraining or fine tuning is needed. In particular, Stochastic-HMDs can detect more than 94% of the evasive malware with a negligible (i.e., < 2%) accuracy loss, along with 15% power savings.