LDL-SCA: Linearized Deep Learning Side-Channel Attack Targeting Multi-tenant FPGAs✱

In recent years, deep-learning side-channel attacks (DL-SCA) have gained increasing attention due to their enhanced efficacy against cryptographic modules. This paper explores that traditional non-profiled DL-SCA is unable to discern correct cipher keys in multi-tenant Field Programmable Gate Array (FPGA) scenarios due to the low correlation between power traces and cipher keys. To address this challenge, we propose Linearized Deep Learning Side-Channel Attack (LDL-SCA). Through modifying the output layer and integrating K-means clustering, LDL-SCA is capable of capturing linear features regardless of the low correlation between input and label. Moreover, we introduce new evaluation metrics derived from R2 and Cohen-kappa score. Our experiments show that LDL-SCA generate results with improved distinguishability, which has about ten times smaller standard deviation and ten times larger peak differences compared with Correlation Power Analysis (CPA) and Linear Regression Analysis (LRA).