A Novel Non-profiling Side-Channel Attack on Masked Devices with Connectivity Matrix

In this paper, we propose a novel pre-processing technique known as the Connectivity Matrix (CM). Building upon the foundation of the CM, we present an effective Second-Order Side-Channel Attack, called Connectivity Matrix Attack (CMA). Our work aims to efficiently counter hardware devices fortified with Masking countermeasures, and it contributes in three significant ways. First, the proposed CM has lower data complexity, as it is constant regarding the number of measurements. Second, we propose the decomposition of the CMs and the utilization of their eigenvalues as feature vectors in CMA. This approach effectively removes noisy components from the CMs and reduces their dimensions. Third, the proposed CMA employs the selected eigenvalues to establish a frequency distribution, followed by a chi-square test. This approach allows CMA to expose both the linear and non-linear leakages present in CMs. The proposed CMA is validated on the public dataset ASCAD and can reveal all the masked bytes successfully. Notably, the concept of the Connectivity Matrix extends beyond the confines of a correlation matrix used in this paper, opening the door to a promising avenue for future research.