Side-channel analysis based on joint moments

Abstract

Side-channel analysis (SCA) is a critical technique employed to evaluate the security of hardware encryption devices by exploiting unintended information leakage during cryptographic operations. This dissertation project focuses on enabling effective SCA in the presence of masking countermeasures. To achieve this, we developed a simulation traces generation framework adaptable to diverse scenario requirements. Furthermore, a preprocessing method was proposed to streamline subsequent experiments by analyzing the joint moment distribution between time sample combinations. Additionally, optimizations were made to the joint moments regression (JMR) based attack method, enhancing its applicability across various scenarios. Finally, by integrating gradient descent training method from neural networks during the training stage, we significantly improved attack speed. These combined approaches resulted in enhanced accuracy.