SAND: A Self-supervised and Adaptive NAS-Driven Framework for Hardware Trojan Detection

TL;DR

SAND is a novel framework that combines self-supervised learning and neural architecture search to improve hardware Trojan detection, offering adaptability, automation, and enhanced accuracy over existing methods.

Contribution

It introduces a self-supervised, NAS-driven approach that automates feature extraction and classifier optimization for robust, adaptable hardware Trojan detection.

Findings

Up to 18.3% improvement in detection accuracy

High resilience against evasive Trojans

Strong generalization across benchmarks

Abstract

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.