Presentation
MetaGuard: Transforming Run-Time Hardware Trojan Detection using Meta Reinforcement Learning
DescriptionDetecting Hardware Trojans (HTs) at run-time presents significant challenges due to the increasing complexity of modern integrated circuits and the dynamic nature of streaming data in IoT-connected systems. Current detection methods often rely on specific benchmarks or focus on limited, predefined Trojan signatures, making it difficult to adapt to new, zero-day (unknown) HTs. Additionally, traditional machine learning-based methods struggle to cope with the variability of side-channel data sources and run-time constraints. In response, we explore the potential of meta-reinforcement learning (meta-RL) as a promising solution. We propose MetaGuard an effective two-step meta-RL framework for adaptive, run-time hardware Trojan detection. In the first step, we leverage meta-learning to incrementally learn from new, unknown data, effectively modeling reinforcement learning environments as multi-armed bandits. In the second step, a Thompson Sampling agent is incorporated to handle the multi-task environment by utilizing priors from recent relative working memory to compute Bayesian posterior distributions. This allows for optimized decision-making across multiple benchmarks, overcoming the limitations of approaches that focus on a single benchmark. MetaGuard is designed to monitor and detect Trojans across uncertain and evolving benchmark variants at run-time streaming data from IoT systems. Experimental results demonstrate that MetaGuard improves detection performance by 13% in F1-score compared to traditional methods, providing a robust and adaptive solution for run-time zero-day HT detection.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby