Side-Channel Power Analysis Based on SA-SVM

Abstract

Support vector machines (SVMs) have been widely used in side-channel power analysis. The selection of the penalty factor and kernel parameter heavily influences how well support vector machines work. Setting reasonable SVM hyperparameters is a key issue in side-channel power analysis. The novel side-channel power analysis method SA-SVM, which combines simulated annealing (SA) and support vector machines (SVMs) to analyze the power traces and crack the key, is proposed in this paper as a solution to this issue. This method differs from other approaches in that it integrates SA and SVMs, enabling us to more effectively explore the search space and produce superior results. In this paper, we conducted experiments on SA-SVM and SVM models from three different aspects: the selection of kernel functions, the number of parameters, and the number of eigenvalues. To compare our results with previous research, we performed experimental evaluations on open datasets. The results indicate that, compared with the SVM model, the SA-SVM model improved the accuracy by 0.25% to 3.25% and reduced the required time by 39.96% to 98.02% when the point of interest was 53, recovering the key using only three power traces. The SA-SVM model outperforms existing methods in terms of accuracy and computation time.

Keywords

• side-channel power analysis
• simulated annealing
• support vector machine
• kernel function