SAND: A Self-supervised and Adaptive NAS-Driven Framework for Hardware Trojan Detection
This addresses security threats in semiconductor supply chains for embedded systems, offering an incremental improvement over existing machine learning-based detection techniques.
The paper tackles the problem of Hardware Trojan detection in embedded systems by proposing SAND, a self-supervised and adaptive NAS-driven framework, which achieves up to 18.3% higher detection accuracy than state-of-the-art methods and shows strong generalization.
The globalized semiconductor supply chain has made Hardware Trojans (HT) a significant security threat to embedded systems, necessitating the design of efficient and adaptable detection mechanisms. Despite promising machine learning-based HT detection techniques in the literature, they suffer from ad hoc feature selection and the lack of adaptivity, all of which hinder their effectiveness across diverse HT attacks. In this paper, we propose SAND, a selfsupervised and adaptive NAS-driven framework for efficient HT detection. Specifically, this paper makes three key contributions. (1) We leverage self-supervised learning (SSL) to enable automated feature extraction, eliminating the dependency on manually engineered features. (2) SAND integrates neural architecture search (NAS) to dynamically optimize the downstream classifier, allowing for seamless adaptation to unseen benchmarks with minimal fine-tuning. (3) Experimental results show that SAND achieves a significant improvement in detection accuracy (up to 18.3%) over state-of-the-art methods, exhibits high resilience against evasive Trojans, and demonstrates strong generalization.