Evidential Deep Fusion for Multi-channel Analysis Against Public-Key Cryptosystems

Side-channel analysis evaluates cryptographic device security, but single channel methods can overlook combined leakage threats. Multi-channel fusion attacks exploit leakage more effectively. In this paper, we propose a decision-level fusion analysis method based on deep learning and Dempster-Shafer evidence theory, specifically tailored for side-channel analysis of public-key algorithms. To evaluate the reliability of sample classification probability distributions, we introduce a metric called the average separability index. Compared to data-level fusion and feature-level fusion, our method yields higher accuracy and confidence for cryptographic operations. In the side-channel analysis of ECC, RSA, and module-lattice-based key encapsulation mechanisms, key recovery accuracy is significantly improved, while the number of traces used is notably reduced. This approach achieves more than 98% cryptographic operation recovery accuracy, improving performance by 5.35%–43.93% over previous methods and boosting the average separability index. Whereas earlier techniques required 20 traces, this fusion method attains full key recovery with a single trace.