Presentation
Beyond Conventional Defenses: Proactive and Adversarial-Resilient Hardware Malware Detection using Deep Reinforcement Learning
DescriptionThis research investigates the vulnerability of ML-enabled Hardware Malware Detection(HMD) methods to adversarial attacks. We introduce proactive and robust adversarial learning and defense based on Deep Reinforcement Learning(DRL). First, highly effective adversarial attacks are employed to circumvent detection mechanisms. Subsequently, an efficient DRL technique based on Advantage Actor-Critic(A2C) is presented to predict adversarial attack patterns in real-time. Next, ML models are fortified through adversarial training to enhance their defense capabilities against both malware and adversarial attacks. To achieve greater efficiency, a constraint controller using Upper Confidence Bounds(UCB) algorithm is proposed that dynamically assigns defense responsibilities to specialized RL agents.
Event Type
Research Manuscript
TimeTuesday, June 252:45pm - 3:00pm PDT
Location3012, 3rd Floor
Security
Hardware Security: Attack and Defense