Paper 2025/857

Classify Directly: A Dynamic Time SPA Classification Method Based on DTW

Abstract

Side-channel analysis remains a critical threat to public-key cryptographic implementations. Simple Power Analysis (SPA) techniques can extract secret keys from a single power trace, often using clustering-based classification methods. However, traces captured in real-world environments often suffer from misalignment and variable trace lengths due to unstable clocks and random delays. As a result, clustering methods are required to use alignment methods that may alter the original information of the traces. To address this problem, this work proposes Dynamic Time Classification (DTC) as an alternative approach to classify cryptographic operations in SPA based on Dynamic Time Warping. Unlike clustering methods, DTC inherently compares power traces without requiring fixed-length segments, which greatly improved the adaptability to unequal traces and thus allows us to classify different operations relatively stably. Experimental results on public-key cryptographic algorithms and post-quantum algorithm implementations show that DTC are no less accurate than clustering methods and are more robust to timing variations. This work also systematically divides the features of different operations and explores the effects of different SPA methods on different types of feature. This work also conducts experiments with and without random delays for different categories, compares the experimental accuracy of different alignment methods, and discusses the feasibility of DTW as a preprocessing method.