Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory

The globalization of integrated circuit (IC) design and fabrication has given rise to severe concerns with respect to modeling strategic interaction between malicious attackers and Hardware Trojan (HT) defenders using game theory. The quantitative assessment of attacker actions has made the game very challenging. In this paper, a novel rough set theory framework is proposed to analyze HT threat. The problem is formulated as an attribute weight calculation and element assessment in an information system without decision attributes. The proposed method introduces information content in the rough set that allows calculation of the weight of both core attributes and non-core attributes. For quantitative assessment, the HT threat is characterized by the closeness coefficient. In order to allow HT defenders to use fast and effective countermeasures, a threat classification method based on the k-means algorithm is proposed, and the Best Workspace Prediction (BWP) index is used to determine the number of clusters. Statistical tests were performed on the benchmark circuits in Trust-hub in order to demonstrate the effectiveness of the proposed technique for assessing HT threat. Compared with k-means, equidistant division-based k-means, and k-means++, our method shows a significant improvement in both cluster accuracy and running time.