Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits known as hardware Trojans 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 and most approaches assume the existence of a “golden model”. Prior works suggest using reverse-engineering to identify such Trojan-free ICs for the golden model but they did not state how to do this efficiently. In this paper, we propose an innovative and robust reverse-engineering approach to identify the Trojan-free ICs. We adapt a well-studied machine learning method, one-class support vector machine, to solve our problem. Simulation results using state-of-the- art tools on several publicly available circuits show that our approach can detect hardware Trojans with high accuracy rate across different modeling and algorithm parameters.