  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 a novel HMDs (Stochastic-HMDs) through approximate
computing, which makes HMDs' inference computation-stochastic, thereby making
HMDs resilient against adversarial evasion attacks. Specifically, we propose to
leverage voltage overscaling to induce stochastic computation in the HMDs
model. We show that such a technique makes HMDs more resilient to both
black-box adversarial attack scenarios, i.e., reverse-engineering and
transferability. Our experimental results demonstrate 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. Moreover, based on
recent results in probably approximately correct (PAC) learnability theory, we
show that Stochastic-HMDs are provably more difficult to reverse engineer.
