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Paper 2021/179

Efficient Framework for Genetic-Algorithm-Based Correlation Power Analysis

An Wang, Yuan Li, Yaoling Ding, Liehuang Zhu, and Yongjuan Wang

Abstract

Various Artificial Intelligence (AI) techniques are combined with classic side-channel methods to improve the efficiency of attacks. Among them, Genetic Algorithms based Correlation Power Analysis (GA-CPA) is proposed to launch attacks on hardware cryptosystems to extract the secret key efficiently. However, the convergence rate is unsatisfactory due to two problems: individuals of the initial population generally have low fitnesses, and the mutation operation is hard to generate high-quality components. In this paper, we give an analysis framework to solve them. Firstly, we employ lists of sorted candidate key bytes obtained with CPA to initialize the population with high quality candidates. Secondly, we guide the mutation operation with lists of candidate keys sorted according to fitnesses, which are obtained by exhausting the values of a certain key byte and calculating the corresponding correlation coefficients with the whole key. Thirdly, key enumeration algorithms are utilized to deal with ranked candidates obtained by the last generation of GA-CPA to improve the success rate further. Simulation experimental results show that our method reduces the number of traces by 33.3\% and 43.9\% compared to CPA with key enumeration and GA-CPA respectively when the success rate is fixed to 90\%. Real experiments performed on SAKURA-G confirm that the number of traces required in our method is much less than the numbers of traces required in CPA and GA-CPA. Besides, we adjust our method to deal with DPA contest v1 dataset, and achieve a better result of 40.76 traces than the winning proposal of 42.42 traces. The computation cost of our proposal is nearly 16.7\% of the winner.