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.

通过分析可疑集成电路 (IC) 的 SC 泄漏异常，侧信道 (SC) 分析被广泛用于实现硬件木马检测 (TD)。然而，当可疑 IC 与 SC 攻击对策 (SCAC) 一起使用以抵抗 SC 攻击时，IC 的 SC 泄漏可能会变得随机。在这种情况下，传统的 SC 分析无法有效检测可疑 IC 中潜在的嵌入木马，因为随机 SC 泄漏可能掩盖木马的关键特征。为了解决这个问题，本文提出了一种基于典型相关分析（CCA）的新型 SC 分析。在CCA的帮助下，随机SC泄漏之间可能的相关性被有效地提取和处理，以区分SCAC和嵌入木马之间的不同特征。结果表明，与之前的 SC 分析相比，所提出的 SC 分析能够将 TD 精度提高 11.3%。