Multi-Modal Side-Channel Analysis Based on Isometric Compression and Combined Clustering
Xiaoyong Kou, Wei Yi Yang, Lunbo Li, Gongxuan Zhang · Symmetry · 2025
Side-channel analysis (SCA) poses a persistent threat to cryptographic hardware by exploiting unintended physical leakages. To address the limitations of traditional single-modality SCA methods, we propose a novel multi-modal side-channel analysis framework that targets the recovery of encryption keys by leveraging the imperfections inherent in hardware implementations. The core objective is to extract and classify information-rich segments from power and electromagnetic (EM) signals in order to recover secret keys without profiling or labeling. Our approach introduces a unified pipeline combining joint peak-based segmentation, isometric compression of variable-length trace segments, and multi-modal feature fusion. A key component of the framework is unsupervised clustering, which serves to automatically classify trace segments corresponding to different cryptographic operations (e.g., different key-dependent leakage classes), thereby enabling key byte hypothesis testing and full key reconstruction. Experimental results on an FPGA-based AES-128 implementation demonstrate that our method achieves up to 99.2% clustering accuracy and successfully recovers the entire encryption key using as few as 1–3 traces. Moreover, the proposed approach significantly reduces sample complexity and maintains resilience in low signal-to-noise conditions. These results highlight the practicality of our technique for side-channel vulnerability assessment and its potential to inform the design of more robust cryptographic hardware.