A machine learning method for hardware Trojan detection on real chips.

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. [ABSTRACT FROM AUTHOR]