SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Abstract

Correlation power analysis (CPA) combined with genetic algorithms (GA) now achieves greater attack efficiency and can recover all subkeys simultaneously. However, two issues in GA-based CPA still need to be addressed: key degeneration and slow evolution within populations. These challenges significantly hinder key recovery efforts. This paper proposes a screening correlation power analysis framework combined with a genetic algorithm, named SFGA-CPA, to address these issues. SFGA-CPA introduces three operations designed to exploit CPA characteristics: propagative operation, constrained crossover, and constrained mutation. Firstly, the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual. Secondly, the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes. Finally, an intelligent search method is proposed to identify optimal parameters, further improving attack efficiency. Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform. In the case of simulation, SFGA-CPA reduces the number of traces by 27.3% and 60% compared to CPA based on multiple screening methods (MS-CPA) and CPA based on simple GA method (SGA-CPA) when the success rate reaches 90%. Moreover, real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.