The State-of-the-art in EDA tools: the Hardware Trojan Confrontation Framework

EDA tools almost completely automate the current chip design. However, EDA tools are all provided by third-party companies, therefore the credibility of EDA tools and the trust issues brought by their process libraries have attracted people's attention. Given these problems, this paper proposes a self-training hardware Trojan confrontation framework based on machine learning embedded in EDA tools. The framework focuses on the gate-level netlist files generated during the integrated chip comprehensive optimization phase, thereby detects, locates and deletes hardware Trojans that may appear. The experimental results show that the framework can achieve more than 85% detection effect for unknown hardware Trojans. Moreover, for known hardware Trojans, this framework can achieve almost 100% detection. Accordingly, hardware Trojans can be located and deleted.