DLSCA: Improving Cross-Device Side-Channel Analysis Using Device Discrepancy Correction
Fanliang Hu, Yi Li, Haowen Tan, Shan Jiang, Zhiyuan Xiao · 2023
In this paper, we focus on the problem of Transfer Learning (TL) in Side-Channel Analysis (SCA) due to differences between the source device and the target device. Such differences are inevitable in practical SCA, but have often been disregarded in recent research. Therefore, the impact of device variation on cross-device SCA is analyzed. A method for Device Discrepancies Correction (DDC) is proposed, which can significantly reduce the discrepancy in sidechannel measurements caused by device variations. This is achieved by calculating the covariance shift between the source device and the target device, and then correcting the side-channel measurements of the target device accordingly. The approach is evaluated on eight Atmel XMEGA128A1U8 microcontrollers as a data pre-processing stage for cross-device SCA, and experimental results show that it can further improve the efficiency of existing cross-device SCA models.