自监督+神经架构搜索，自动检测硬件木马
SAND: A Self-supervised and Adaptive NAS-Driven Framework for Hardware Trojan Detection
- 用自监督学习自动提取特征，不用人工设计
- 结合神经架构搜索，检测准确率提升18.3%
- 适应新攻击类型，适合嵌入式安全场景
全球化半导体供应链使硬件木马（HT）成为嵌入式系统的重要安全威胁，亟需高效且可适应的检测机制。尽管已有基于机器学习的HT检测方法，但其依赖随意特征选择且缺乏自适应能力，难以应对多样化的木马攻击。本文提出SAND框架，一种自监督与神经架构搜索（NAS）驱动的高效硬件木马检测方法。首先，采用自监督学习（SSL）实现特征自动化提取，摆脱对人工特征工程的依赖；其次，集成神经架构搜索动态优化下游分类器，实现对未见基准的无缝适应，仅需少量微调；实验表明，SAND在检测准确率上相比现有最优方法提升最高达18.3%，对规避型木马具有高鲁棒性，并展现出强泛化能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。