Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackSimilar Papers 제목 키워드 기반
Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation
Hardware-based Malware Detectors (HMDs) using Machine Learning (ML) models have shown promise in detecting malicious workloads. However, the conventional black-box based machine learning (ML) approach used in these HMDs …
BIG-bench Machine LearningManagementTowards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation
This study introduces RT-HMD, a Hardware-based Malware Detector (HMD) for mobile devices, that refines malware representation in segmented time-series through a Multiple Instance Learning (MIL) approach. We address the m…
Malware DetectionMultiple Instance LearningTime SeriesAdversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective
Machine learning-based malware detectors are increasingly vulnerable to adversarial examples. Traditional defenses, such as one-shot adversarial training, often fail against adaptive attackers who use reinforcement learn…
Reinforcement LearningBilevel OptimizationMalware DetectionExpression-aware video inpainting for HMD removal in XR applications
Head-mounted displays (HMDs) serve as indispensable devices for observing extended reality (XR) environments and virtual content. However, HMDs present an obstacle to external recording techniques as they block the upper…
Facial Expression RecognitionFacial Expression Recognition (FER)Video InpaintingTowards resilient machine learning for ransomware detection
There has been a surge of interest in using machine learning (ML) to automatically detect malware through their dynamic behaviors. These approaches have achieved significant improvement in detection rates and lower false…
BIG-bench Machine LearningGenerative Adversarial NetworkMalware Analysis