On Reverse Engineering-Based Hardware Trojan Detection

Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits (ICs), also known as hardware Trojans (HTs), has attracted attention in academia as well as industry. To reduce the risks associated with Trojans, researchers have proposed different approaches to detect them. Among these approaches, test-time detection approaches have drawn the greatest attention. Many test-time approaches assume the existence of a Trojan-free (TF) chip/model also known as “golden model.” Prior works suggest using reverse engineering (RE) to identify such TF ICs for the golden model. However, they did not state how to do this efficiently. In fact, RE is a very costly process which consumes lots of time and intensive manual effort. It is also very error prone. In this paper, we propose an innovative and robust RE scheme to identify the TF ICs. We reformulate the Trojan-detection problem as clustering problem. We then adapt a widely used machine learning method, ${K}$ -means clustering, to solve our problem. Simulation results using state-of-the-art tools on several publicly available circuits show that the proposed approach can detect HTs with high accuracy rate. A comparison of this approach with our previously proposed approach [1] is also conducted. Both the limitations and application scenarios of the two methods are discussed in detail.