Efficient algorithmic evaluation of correlation power analysis: Key distinguisher based on the correlation trace derivative

Abstract

Correlation power analysis (CPA) is one of the most common side-channel attacks today, posing a threat to many modern ciphers, including AES. In the final step of this attack, the cipher key is usually extracted by the attacker by visually examining the correlation traces for each key guess. The naïve way to extract the correct key algorithmically is selecting the key guess with the maximum Pearson correlation coefficient.

We propose another key distinguisher based on a significant change in the correlation trace rather than on the absolute value of the coefficient. Our approach performs better than the standard maximization, especially in the noisy environment, and it allows to significantly reduce the number of acquired power traces necessary to successfully mount an attack in noisy environment, and in some cases make the attack even feasible.