A UMAP-based Clustering Side-Channel Analysis on Public-Key Cryptosystems

Horizontal analysis is a widely adopted method in side-channel analysis, particularly for public-key cryptosystems, where attackers aim to recover the key from a single trace. Current methods rely on trace segmentation, dimensionality reduction, and classification, but high noise and poor feature preservation hinder accuracy. Noise blurs cryptographic operation segment boundaries, and existing dimensionality reduction techniques fail to maintain the inherent distribution of trace points in a high-dimensional space. As a result, secret information recovery based on clustering remains inaccurate. This paper proposes an automated horizontal analysis framework named UMAP-HC to improve secret information recovery accuracy. The framework employs a sliding segmentation method to locate cryptographic operations in noisy traces with blurred segment boundaries. It leverages uniform manifold approximation and projection (UMAP) for feature preservation and hierarchical clustering for secret information recovery. Experimental results on four open access public-key algorithm power trace datasets, an SM2 power trace collected from a smart card, and an ECC power trace with dummy operation countermeasures demonstrate that UMAP-HC effectively classifies cryptographic operations, accurately locates operation segments, and recovers secret key. It achieves up to 100% recovery accuracy, surpassing previous methods by 40%-70%, with normalized mutual information reaching 1, an improvement of 0.02-0.99 over existing approaches.