Beyond Conventional Defenses: Proactive and Adversarial-Resilient Hardware Malware Detection using Deep Reinforcement Learning

This research investigates the vulnerability of machine learning-enabled Hardware Malware Detection (HMD) methods to adversarial attacks, a pressing concern undermining their efficacy against malware threats. While prior adversarial learning research primarily centered on image classification and/or overlooked adversarial attacks in HMDs, we delve into the distinctive challenges posed by adversarial attacks in the context of tabular data from processors' performance counters. This paper introduces a proactive and robust multi-phased adversarial learning and defense framework based on Deep Reinforcement Learning (DRL). In the initial phase, highly effective adversarial attacks are employed to circumvent ML-based detection mechanisms. Subsequently, an efficient deep reinforcement learning technique based on Advantage Actor Critic (A2C) is presented to predict adversarial attack patterns in realtime. Next, ML models are fortified through adversarial training to enhance their defense capabilities against both malware and adversarial attacks. To achieve greater efficiency, an RL-based constraint controller using an Upper Confidence Bounds (UCB) algorithm is proposed that dynamically assigns adversarial defense responsibilities to specialized RL agents based on different performance constraints. The results demonstrate the proposed framework's effectiveness, indicating up to 86% boost in F1-score for defending against adversarial attacks across all models, leading to detection rate of 96.1% for the top-performing adaptive malware detector.