HTDet: A Clustering Method Using Information Entropy for Hardware Trojan Detection
HTDet: A Clustering Method Using Information Entropy for Hardware Trojan Detection
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DOI：
10.26599/tst.2019.9010047
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发表时间：
2021-02-01
影响因子：
6.6
通讯作者：
Li, Xiaowei
中科院分区：
文献类型：
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作者：
Lu, Renjie;Shen, Haihua;Li, Xiaowei
硬件木马（Hardware Trojans，HTs）由于其潜在的巨大威胁，引起了学术界和工业界越来越多的关注。在本文中，我们提出了HTDet，一种新的HT检测方法，使用基于信息熵的聚类。为了保持较高的隐蔽性，通常在低可控性和低可观测性的区域插入HT，这将导致特洛伊逻辑在仿真过程中具有极低的转换。这意味着具有低跃迁的区域将为HT检测提供更丰富和更重要的信息。HTDet应用信息论技术和基于密度的聚类算法（称为基于密度的噪声应用空间聚类（DBSCAN））来检测检测电路中所有可疑的木马逻辑。DBSCAN是一种无监督的学习算法，可以提高HTDet的适用性。此外，我们开发了一个启发式的测试模式生成方法，使用互信息，以增加可疑的木马逻辑的转换。电路基准测试实验验证了HTDet的有效性。
Hardware Trojans (HTs) have drawn increasing attention in both academia and industry because of their significant potential threat. In this paper, we propose HTDet, a novel HT detection method using information entropy-based clustering. To maintain high concealment, HTs are usually inserted in the regions with low controllability and low observability, which will result in that Trojan logics have extremely low transitions during the simulation. This implies that the regions with the low transitions will provide much more abundant and more important information for HT detection. The HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect all suspicious Trojan logics in the circuit under detection. The DBSCAN is an unsupervised learning algorithm, that can improve the applicability of HTDet. In addition, we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan logics. Experiments on circuit benchmarks demonstrate the effectiveness of HTDet.