HypSCA: A Hyperbolic Embedding Method for Enhanced Side-Channel Attack

Deep learning-based side-channel attack (DLSCA) has become the dominant paradigm for extracting sensitive information from hardware implementations due to its ability to learn discriminative features directly from raw side-channel traces. A common design choice in DLSCA involves embedding traces in Euclidean space, where the underlying geometry supports conventional objectives such as classification or contrastive learning. However, Euclidean space is fundamentally limited in capturing the multi-level hierarchical structure of side-channel traces, which often exhibit both coarse-grained clustering patterns (e.g., Hamming weight similarities) and fine-grained distinctions (e.g., instruction-level variations). These limitations adversely affect the discriminability of learned representations, particularly across diverse datasets and leakage models. In this work, we propose HypSCA, a dual-space representation learning method that embeds traces in hyperbolic space to exploit its natural ability to model hierarchical relationships through exponential volume growth. In contrast to existing approaches, HypSCA jointly combines hyperbolic structure modeling with local discriminative learning in Euclidean space, enabling the preservation of global hierarchies while enhancing fine-grained feature separation. Quantitative geometric analysis shows measurable hierarchical organization, most clearly under the HW model. Consequently, extensive experiments demonstrate that HypSCA achieves superior attack performance, outperforming SOTA methods by up to 30.8%.