Security Analysis of Temporal Convolutional Network-Based Side-Channel Attacks for AES Cryptographic Implementations

Profiling side-channel attacks based on deep learning can recover cryptographic information from power-consumption traces, but their effectiveness may be reduced when desynchronisation displaces informative leakage across temporal positions. This study investigates whether Temporal Convolutional Networks (TCNs) can model the local and long-range dependencies present in desynchronised traces. Three complementary TCN variants are designed to isolate different temporal modelling strategies: a single-kernel dilated architecture (TCN1), a multi-scale architecture using parallel kernel sizes (TCN2), and a residual architecture intended to support stable hierarchical feature learning (TCN3). The models are evaluated on the ASCAD v1 fixed-key benchmark under synchronised conditions and maximum temporal shifts of 25, 50, and 75 samples, using validation loss and complementary key-ranking metrics. Under the most challenging setting, TCN1 achieves the highest Rank Success Rate and reaches its best rank substantially earlier than the conventional CNN baselines, although the lowest Final Rank is obtained by a CNN. These results indicate that TCN1 provides the most favourable trade-off among convergence speed, ranking consistency, and architectural complexity, without establishing uniform TCN superiority across all metrics. Gradient saliency, LIME, and occlusion analyses further identify temporal regions that influence the model predictions and are consistent with expected leakage patterns. The main contribution is a controlled comparison of complementary TCN design strategies, combined with an explainability analysis, for profiling side-channel attacks under trace desynchronisation.