Deep learning methods have significantly impact in the side-channel attack (SCA) community. However, the training and verification phases of deep learning-based side-channel attacks (DL-SCA) typically focus on a single byte, which leads to the requirement of training numerous models to recover all partial key bytes. To resolve the problem, this paper proposes the TripM model, triple-keys attack model, which can attack three bytes in a single training session. First, TripM leverages label groups black to learn multiple bytes of leaked information in a single training session, where the label gr...
