Unsupervised side-channel power analysis based on invariant information clustering

Abstract — Side-channel analysis (SCA) has emerged as a research hotspot in the field of cryptanalysis. Among various approaches, unsupervised deep learning-based methods demonstrate powerful information extraction capabilities without requiring labeled data. However, existing unsupervised methods, particularly those represented by differential deep learning analysis (DDLA) and its improved variants, while overcoming the dependency on labeled data inherent in template analysis, still suffer from high time complexity and training costs when handling key byte difference comparisons. To address this issue, this paper introduces invariant information clustering (IIC) into SCA for the first time, proposing a novel unsupervised learning-based SCA method, named IIC-SCA. By leveraging mutual information maximization techniques for automatic feature extraction of power leakage data, our approach achieves key recovery through a single training session, eliminating the prohibitive computational overhead of traditional methods that require separate training for all possible key bytes. Experimental results on the ASCAD dataset demonstrate successful key extraction using only 50000 training traces and 2000 attack traces. Furthermore, compared with DDLA, the proposed method reduces training time by approximately 93.40% and memory consumption by about 6.15%, significantly decreasing the temporal and resource costs of unsupervised SCA. This breakthrough provides new insights for developing low-cost, high-efficiency cryptographic attack methodologies.