An Intelligent Framework for Cluster-Based Side-Channel Analysis on Public-Key Cryptosystems

Classical cluster-based side-channel analysis (SCA) uses clustering algorithms to analyze power traces and often, principal component analysis to reduce the dimension of data, resulting in that clustering may not deal well with high-dimensional traces, such as cryptographic algorithm implementations with countermeasures. In this article, we propose an intelligent framework for cluster-based SCA, which includes three steps of clustering, classification and correction, for processing large high-dimensional data. By combining unsupervised clustering and supervised deep learning techniques, the framework succeeds in mining the data for additional in-depth information. In addition, unlike traditional cluster-based SCA, our approach focuses on deep learning and deliberately avoids over-reliance on cluster labels during classification. And metrics for correction are adopted to achieve a high level of reliability in key recovery. Experiments on the RSA smart card based on Montgomery ladder implementation and FPGA-based ECC with random delay demonstrate that our framework can significantly improve the success rate with strong robustness.