Speeding Up SPA-GPT: Efficient Automated Trace Segmentation for Simple Power Analysis with FFT-Based Downsampling

Simple Power Analysis (SPA) remains a practical threat to public-key cryptographic implementations, as distinguishable power-consumption patterns may reveal secret keys from a single trace. At CHES 2024, SPA-GPT was proposed as a reinforcement-learning-based method for automated trace segmentation using only the key length. However, its high computational cost can delay post-acquisition single-trace SPA analysis, especially for long traces of public-key cryptographic operations, where segmentation may take tens of minutes. In this paper, we present an efficient automated SPA analysis framework that accelerates trace segmentation while preserving key-recovery accuracy. The framework first applies Fast Fourier Transform (FFT)-based frequency-domain downsampling to obtain a compact trace representation for fast boundary search. Candidate boundaries are located in the compressed domain and then mapped back to the original trace for local refinement, reducing segmentation overhead while preserving the leakage information required for classification and key recovery. In addition, optimizing the reward computation in SPA-GPT and introducing partial pretraining reduce redundant distance calculations and repeated training overhead in reinforcement-learning-based segmentation. Experimental results show that, on five public SPA datasets and one independently collected ECC trace dataset, the proposed method reduces segmentation runtime by 57.07% to 82.09% while maintaining comparable key-recovery accuracy, and achieves up to 100% key-recovery success on applicable datasets.