An efficient methodology for hardware Trojan detection based on canonical correlation analysis

Abstract

Side-channel (SC) analyses are extensively used for achieving hardware Trojan detection (TD) by analyzing the anomalies of SC leakages of the suspicious integrated circuits (ICs). However, when a suspicious IC is employed with an SC attack countermeasure (SCAC) to resist against SC attacks, the SC leakages of the IC may become random. Under such a circumstance, the conventional SC analyses are not efficient in detecting the potential embedded Trojans within the suspicious IC since the random SC leakages may mask the critical features of the Trojans. To address this issue, a novel SC analysis based on canonical correlation analysis (CCA) is proposed in this paper. The possible correlations among the random SC leakages are efficiently extracted and processed to distinguish the different features between the SCAC and the embedded Trojans under the assistance of CCA. Result shows that the proposed SC analysis is able to enhance the TD accuracy by 11.3% as compared to the previous SC analyses.