Comparative Deep Learning-Based Side-Channel Analysis of an FPGA-Based CRYSTALS-Kyber NTT Accelerator

Deep learning-based side-channel analysis is one of the most effective techniques for extracting and classifying sensitive information from a target device. This paper demonstrates the best-performing deep learning model for the target implementation by evaluating various deep learning architectures, including MLP, CNN, and RNN, while systematically optimizing their hyperparameters to achieve the best performance. The paper uses a case study of the Number Theoretic Transform accelerator for the CRYSTALS-Kyber key encapsulation mechanism to show that enhanced deep learning analysis can be used to break security. The best-performing deep learning-based model achieved a 96.64% accuracy in classifying pairwise coefficients of the s vector, which is used to generate the secret key with the NTT accelerator for Kyber768 and Kyber1024. For Kyber512, the model achieved an accuracy of 95.71%. The proposed approach significantly improves average training efficiency, with POIs achieving up to 1.45 times faster performance for MLP models, 10.53 times faster for CNNs, and 10.28 times faster for RNNs compared to deep learning methods without POIs, while maintaining high accuracy in side-channel analysis.