Profiling Side Channel Analysis with Deep Learning Based on Boosting Algorithm

This paper proposes SCABoosting, a novel boosting framework for deep learning-based Side-Channel Analysis (SCA) that significantly enhances key recovery efficiency while maintaining computational lightweightness. Our method integrates multiple randomly generated CNN classifiers through a sequential boosting strategy, achieving superior performance with only 26 M parameters compared to 68 M parameters required by state-of-the-art CNNbest models. Extensive evaluation on three standard datasets demonstrates compelling results: on ASCAD dataset, our approach reduces guessing entropy (GE) to 102 (vs 204 for CNNbest) and achieves 92