Abstract: Side-channel analysis faces the challenge of extracting key-related features from a large number of power traces.Since MHA(Multi-Head Attention) mechanism enables multiple heads to capture both local dependencies and global correlations in data,it has strong multi-feature learning capabilities.Therefore,MHA has been widely applied to automatic feature extraction in side-channel analysis in recent years.However,when multiple heads learn features simultaneously,MHA is prone to the limitations of low-rank bottlenecks and redundant heads.This problem weakens the ability of MHA to capture complex temporal dependencies and global feature relationships in data,leading to instability during training and difficulty in converging to the optimal solution.To address this issue,this paper proposes a deep side-channel analysis method based on dynamically composable multi-head attention.The method introduces a dynamically composable multi-head attention mechanism that adaptively combines information from different attention heads,thereby effectively enhancing the model’s capability to extract key features,ensuring training stability,and continuously improving attack performance.Comparative experiments conducted on the public ASCAD,AES_HD,and CHES18 datasets demonstrate that the proposed method outperforms existing models in both training stability and attack effectiveness.For example,on the AES_HD and CHES18 datasets,the number of power traces required for successful attacks is reduced by 56.2% and 66.7%,respectively.

Key words: Side-channel analysi, Deep learning, Feature extraction, Multi-head attention, Dynamically composable multi-head attention