Second-Order Side-Channel Analysis Based on Orthogonal Transform Nonlinear Regression

Abstract

In recent years, side-channel analysis technology has been one of the greatest threats to information security. SCA decrypts the key information in the encryption device by establishing an appropriate leakage model. As one of many leakage models, the XOR operation leakage proposed by linear regression has typical representative significance in side-channel analysis. However, linear regression may have the problem of irreversibility of a singular matrix in the modeling stage of template analysis and the problem of poor data fit in the template analysis after the cryptographic algorithm is masked. Therefore, this paper proposes a second-order template analysis method based on orthogonal transformation nonlinear regression. The irreversibility of a singular matrix and the inaccuracy of the model are solved by orthogonal transformation and adding a negative direction to the calculation of the regression coefficient matrix. In order to verify the data fitting effect of the constructed template, a comparative experiment of template analysis based on regression, Gaussian, and clustering was carried out on SAKURA-G. The experimental results show that the second-order template analysis based on orthogonal transformation nonlinear regression can complete key recovery without sacrificing the performance of regression estimation. Under the condition of high noise and high order template analysis, the established template has good universality.

Secure image encryption algorithm using chaos-based block permutation and weighted bit planes chain diffusion

“…The results provide a solid foundation and practical solutions for chaos generation and image encryption. 36 , 37 , 38 , 39 These achievements mean that many encryption algorithms have shown satisfactory results in several areas, 40 , 41 , 42 , 43 which have significantly advanced the field of information security technology. 44 , 45 , 46 , 47 However, as society develops, the exponential growth of information poses significant challenges to previous algorithms.…”