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 reverseengineering 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-ofthe-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.

Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits, also 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. Many test-time approaches assume the existence of a Trojan-free chip/model also known as "golden model". Prior works suggest using reverseengineering to identify such Trojan-free ICs for the golden model. However, they did not state how to do this efficiently. In fact, reverse-engineering 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 reverse-engineering scheme to identify the Trojan-free 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 stateof-the-art tools on several publicly available circuits show that the proposed approach can detect hardware Trojans 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.Index Terms-Hardware Trojan detection, reverse-engineering based hardware Trojan detection, integrated circuit (IC) security and trust, one-class SVM, K-Means clustering 0278-0070 (c)

Isr develops, applies and teaches advanced methodologies of design and analysis to solve complex, hierarchical, heterogeneous and dynamic problems of engineering technology and systems for industry and government.Isr is a permanent institute of the university of maryland, within the a. James clark school of engineering. It is a graduated national science foundation engineering research center. Abstract-The hardware Trojan threat has motivated development of Trojan detection schemes at all stages of the integrated circuit (IC) lifecycle. While the majority of existing schemes focus on ICs at test-time, there are many unique advantages offered by post-deployment/run-time Trojan detection. However, run-time approaches have been underutilized with prior work highlighting the challenges of implementing them with limited hardware resources. In this paper, we propose innovative lowoverhead approaches for run-time Trojan detection which exploit the thermal sensors already available in many modern systems to detect deviations in power/thermal profiles caused by Trojan activation. Simulation results using state-of-the-art tools on publicly available Trojan benchmarks verify that our approaches can detect active Trojans quickly and with few false positives.

The hardware Trojan threat has motivated development of Trojan detection schemes at all stages of the integrated circuit (IC) lifecycle. While the majority of existing schemes focus on ICs at test-time, there are many unique advantages offered by post-deployment/run-time Trojan detection. However, run-time approaches have been underutilized with prior work highlighting the challenges of implementing them with limited hardware resources. In this paper, we propose three innovative low-overhead approaches for run-time Trojan detection which exploit the thermal sensors already available in many modern systems to detect deviations in power/thermal profiles caused by Trojan activation. The first one is a local sensor-based approach that uses information from thermal sensors together with hypothesis testing to make a decision. The second one is a global approach that exploits correlation between sensors and maintains track of the ICs thermal profile using a Kalman filter (KF). The third approach incorporates leakage power into the system dynamic model and apply Extended Kalman Filter (EKF) to track IC's thermal profile. Simulation results using state-ofthe-art tools on 10 publicly available Trojan benchmarks verify that all three proposed approaches can detect active Trojans quickly and with few false positives. Among three approaches, EKF is flawless in terms of the 10 benchmarks tested but would require the most overhead.

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