Hardware Trojan: Malware Detection Using Reverse Engineering and SVM

Abstract

Due to the globalization, advanced information and simplicity of computerized frameworks have left the substance of the advanced media greatly unreliable. Security concerns, particularly for integrated circuits (ICs) and systems utilized as a part of critical applications and cyber infrastructure have been encountered due to Hardware Trojan. In last decade Hardware Trojans have been investigated significantly by the research community and proposed solution using either test time analysis or run time analysis. Test time analysis uses a reverse engineering based approach to detect Trojan which, limits to the destruction of ICs in detection process. This paper explores Hardware Trojans from the most recent decade and endeavors to catch the lessons learned to detect Hardware Trojan and proposed an innovative and powerful reverse engineering based Hardware Trojan detection method using Support Vector Machine (SVM). SVM uses benchmark golden ICs for training purpose and use them for the future detection of Trojan infected ICs. Simulation process of proposed method was carried out by utilizing state-of-art tools on openly accessible benchmark circuits ISCAS 85 and ISCAS 89 and demonstrates Hardware Trojans detection accuracy using SVM over different kernel functions. The results show that Radial kernel based SVM performs better among linear and polynomial.