Fault Probability Correlation Analysis Based on Secondary Filtering

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 104 times.