Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches.

针对硬件木马 (HT) 的安全性是集成电路 (IC) 设计和制造中的一个重要问题。目前大多数HT检测方法都是基于电路设计的黄金模型。此外，一些方法需要用于 HT 激活的测试模式。在本文中，我们提出了SC-COTD（硬件木马检测的顺序/组合可控性和可观察性特征），这是一种有效的硬件木马检测，可以摆脱黄金芯片和测试模式的限制。 SC-COTD使用顺序和组合可测试性措施通过机器学习方法检测和定位 HT 信号。该方法部署了一个基于 k-means 聚类的集成分类器。聚类模型在可测试性特征以及聚类大小方面具有多种多样性，这些特征可以检查和揭示协作方案常规网表的不同方面。聚类结果被过滤，然后输入到基于多数投票的决策程序中，以消除每个模型的有限缺陷。TrustHUB 基准的评估结果表明，SC-COTD 可以 100% 检测和定位 HT，没有任何假阴性，即 Recall = 1。虽然我们的方法具有有限数量的假阳性，但与众所周知的先前方法相比，它具有最佳性能。