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Can Overclocking Detect Hardware Trojans?

Hardware Trojans can take various forms to manifest an integrated circuit (IC), causing altered functional behavior, and potential critical consequences, e.g., leaking secret information in encryption applications. This paper presents an approach that uses over-clocking to produce different bit flip patterns between clean design and Trojan-inserted design. Consequently, we apply machine learning algorithms to learn the bit flips distribution at the output of an IC, and therefore differentiate the divergence in the pattern of bit flips caused by the Trojan in IC from its baseline distribution. This approach is effective in detecting Trojan placed off the critical path. The proposed technique is evaluated on benchmarks from Trust-hub and show a detection accuracy of 87%.