Side-Channel Analysis Based on Multiple Leakage Models Ensemble

Summary

Researchers propose a side-channel analysis framework that ensembles neural networks trained under multiple leakage models, using Bagging to combine complementary features from heterogeneous models. The authors use information entropy and Jensen's inequality to prove the ensemble's predictive distribution approximates the true key distribution more closely. They report that the framework improves key recovery efficiency and robustness and generalizes across platforms and adversarial environments.