In an era where cryptographic devices are widely used, research on Side-Channel Analysis (SCA) has attracted significant attention. While Deep Learning-based SCA (DLSCA) demonstrates notable advantages with the rapid development of deep learning, its performance remains constrained by reliance on single leakage model, leading to inefficient attacks and inadequate information utilization–particularly in grey-box scenarios where optimal leakage point identification proves challenging. Traditional ensemble methods attempting multiple leakage points combination are limited to either homogeneous models or manual feature fusion, failing to address the collaborative optimization of heterogeneous leakage features. To overcome these limitations, we propose a side-channel analysis framework based on multiple leakage models ensemble. This approach defines the training process of neural networks under different leakage models as base learners and employs Bagging ensemble learning to combine complementary leakage characteristics from heterogeneous leakage models. Thereby, it collaboratively optimizes multi-dimensional physical leakage features, significantly improving the efficiency and robustness of key recovery. Furthermore, based on information entropy and Jensen's inequality, we rigorously prove that the predictive distribution of the ensemble model more closely approximates the true key distribution, providing theoretical support for multiple models ensemble. Experimental results show that the proposed modular ensemble framework supports flexible combinations of arbitrary leakage models and exhibits excellent generalization capability and stability across different platforms and adversarial environments.

Post-quantum cryptography (PQC) has become the most promising cryptographic scheme against the threat of quantum computing to conventional public-key cryptographic schemes. Saber, as the finalist in the third round of the PQC standardization procedure, presents an appealing option for embedded systems due to its high encryption efficiency and accessibility. However, side-channel attack (SCA) can easily reveal confidential information by analyzing the physical manifestations, and several works demonstrate that Saber is vulnerable to SCAs. In this work, a ciphertext comparison method for masking design based on the bitslicing technique and zerotest is proposed, which balances the tradeoff between the performance and security of comparing two arrays. The mathematical description of the proposed ciphertext comparison method is provided, and its correctness and security metrics are analyzed under the concept of PINI. Moreover, a high-order masking approach based on the state of the art, including the hash functions, centered binomial sampling, masking conversions, and proposed ciphertext comparison, is presented, using the bitslicing technique to improve throughput. As a proof of concept, the proposed implementation of Saber is on the ARM Cortex-M4. The performance results show that the runtime overhead factor of 1st-, 2nd-, and 3rd-order masking is 3.01×, 5.58×, and 8.68×, and the dynamic memory used for 1st-, 2nd-, and 3rd-order masking is 17.4kB, 24.0kB, and 30.2kB, respectively. The SCA-resilience evaluation results illustrate that the 1st-order Test Vectors Leakage Assessment (TVLA) result fails to reveal the secret key with 100,000 traces.