Abstract
Due to the global supply chain of integrated circuits (IC) from design to application, Hardware Trojan (HT) may be stealthily inserted into ICs. The effect of HT detection methods are related to the signal-to-noise ratio (SNR) and the Trojan-to-circuit ratio (TCR). Various HT detection methods are designed to target at simulated circuits; however, the effect on real chips is not involved. In the light of detection of HT on real chips with low SNR and low TCR, a machine learning method is proposed and experimented in this paper. It is difficult to directly distinguish the insignificant effect of HT on a modern complex chip. The proposed method extracts statistic features to explore the much rich expression of HT signals and adjusts the distance of different features to separate Trojan circuits from the security ones far apart. The proposed methods are tested on real chips with 10−5 TCR and demonstrated the effect compared to other state-of-the-art methods.
Highlights
The security of integrated circuits (IC) plays a very significant role in military, economy, communication, and other fields.1,2 Due to the outsourcing of fabrication to foundries, utilization of thirdparty electronic design automation tools, and incorporation of thirdparty intellectual property cores into the design of an integrated circuit, the chip may involve surreptitious and malicious modifications, known as Hardware Trojans (HTs).3 The activated HT can change IC functionality, reduce the IC reliability, leak system confidential information, and even may pose a serious threat to the national security
The improvement from the K-Nearest Neighbors (KNN) with statistical features to the Support Vector Machine (SVM) with statistical features demonstrates the effectiveness of the proposed distance learning
One can observe that the proposed method has the best confusion matrix, the largest number in diagonals and the smallest number in off-diagonals, which demonstrated the efficiency of the proposed method
Summary
The security of integrated circuits (IC) plays a very significant role in military, economy, communication, and other fields. Due to the outsourcing of fabrication to foundries, utilization of thirdparty electronic design automation tools, and incorporation of thirdparty intellectual property cores into the design of an integrated circuit, the chip may involve surreptitious and malicious modifications, known as Hardware Trojans (HTs). The activated HT can change IC functionality, reduce the IC reliability, leak system confidential information, and even may pose a serious threat to the national security. The third class is to analyze the side-channel signal, which refers to measuring circuit parameters during post-silicon testing, such as power consumption, electromagnetic emanation, path delay, temperature, and radiation proles, to distinguish a Trojan-infected IC from the benign ones. This analysis fully utilizes the advantage of the parametric variations in side-channel information created by extra circuitry and/or the activities from HTs. The effect of a Trojan on a side-channel feature can be masked by process variations (PVs), environmental noise, and measurement noise.
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