Side-Channel Analysis Based on Multiple Leakage Models Ensemble

This research proposes a new framework for side-channel analysis (SCA, a type of attack that exploits physical information like power consumption or timing to break cryptography) by combining multiple different leakage models (ways of measuring how a cryptographic device leaks secrets) using ensemble learning (combining many weaker models into one stronger one). The framework improves how well attackers can recover secret keys by using deep learning with complementary information from different measurement approaches, and the authors prove mathematically that their ensemble model gets closer to the true secret distribution.