Focusing on the Key Suspicious Trojan Nets with a Collaborative Approach

While most gate-level hardware Trojan detection techniques strive to detect as many as possible suspicious nets, this paper suggests another direction: identifying only a few suspicious nets, in order to reduce the subsequent manual investigation effort, since there is no need to trace multiple suspicious nets that lead to the same Trojan module. To accomplish this goal, we adopt a collaborative approach by a combination of structural-based analysis, testability-based analysis, and behavioral-based analysis to minimize the number of suspicious Trojan nets. Extensive experiments are conducted with Trust-HUB benchmark and an industrial processor. The results are very significant: (1) high precision 95.39%, most of identified nets being actual Trojan nets; (2) high true negative rate 99.99%, most normal nets being correctly identified as non-suspicious; (3) 44% less suspicious nets to greatly reduce the subsequent manual investigation effort; while (4) leading to detect 100% of the Trojan modules.