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

• URL: http://arxiv.org/abs/2103.06936v1
• Date: Thu, 11 Mar 2021 20:18:40 GMT
• Title: Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling
• Authors: Md Shohidul Islam, Ihsen Alouani, Khaled N. Khasawneh
• Abstract summary: Machine learning-based hardware malware detectors (HMDs) offer a potential game changing advantage in defending systems against malware. HMDs suffer from adversarial attacks, can be effectively reverse-engineered and subsequently be evaded, allowing malware to hide from detection. We propose a novel HMDs (Stochastic-HMDs) through approximate computing, which makes HMDs resilient against adversarial evasion attacks.
• Score: 3.5803801804085347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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