Side-Channel Analysis (SCA) utilizing Deep learning has demonstrated significant potential in recovering secret keys from cryptographic implementations. However, the efficiency of these attacks is often severely compromised by hardware countermeasures such as temporal shuffling, which desynchronizes leakage traces. Existing non-profiled collision attacks successfully mitigate shuffling, but often rely on a “Grey-Box” threat model, requiring prior knowledge of the shuffle permutation to align traces before analysis. This study presents a Global Average Pooling Convolutional Neural Network (GAP-CNN) designed to exploit side-channel collisions in a strict Black-Box setting. By integrating a translation-invariant GAP layer, the proposed architecture forces the network to learn the presence of leakage signatures regardless of their temporal location, effectively neutralizing the shuffling countermeasure end-to-end without pre-processing. The methodology is evaluated on the DPA Contest v4.2 dataset, a highly protected AES-128 implementation. The empirical results demonstrate that the proposed Black-Box approach successfully recovers a majority of the target bytes, outperforming previous Grey-Box baselines. Furthermore, the study demonstrates strong cross-byte portability and cross-dataset robustness against masking countermeasures (ASCAD), confirming the existence of exploitable leakage clusters that persist despite advanced randomization.
Ismail et al. (Thu,) studied this question.