Chosen message strategy to improve the correlation power analysis

Ouladj, Maamar; Guillot, Phillipe; Mokrane, Farid

doi:10.1049/iet-ifs.2018.5103

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Yongdae et al [13] proposed a preprocessing technique to select a subset with higher correlation factors from the power trace set and then conduct CPA. In [14], the plaintext is selected in both non-adaptive and adaptive ways, and the original optimization of the standard CPA is carried out, which reduces the number of power traces needed to recover the key. All the above methods improve the computational efficiency of CPA, but these papers do not analyze the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Fault Probability Correlation Analysis Based on Secondary Filtering

Wu,

Zhou,

et al. 2023

IEEE Access

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Correlation power analysis (CPA) is a classical method in side-channel attacks. Based on the power consumption model, the correlation between the power consumption of cryptographic devices and the assumed intermediate value is analyzed to recover the key. Theoretically, only a few power traces are required to recover the key when the noise hypothesis is known. However, in the high-frequency and high-noise environment, the completion of CPA requires more power traces, and the computational complexity also increases. Therefore, this paper proposes a fault probability correlation analysis method based on secondary filtering (2F-FPCA), which selects the fault probability traces according to the Hamming Weight of the intermediate value and reduces the number of sampling points by selecting points of interest. This method does not need to access ciphertext and is little affected by noise. Moreover, it can recover the key with fewer fault probability traces and lower computational complexity, improving the attack efficiency of CPA. In this paper, 2F-FPCAs are carried out based on the AES-128 algorithm of the Micro Controller Unit (MCU). The key can be recovered successfully using 10 fault probability traces, and the computational complexity is reduced by 10 4 times.

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Section: Introductionmentioning

confidence: 99%