Informatica

Advanced Encryption Standard (AES) block cipher system is widely used in cryptographic applications. A nonlinear substitution operation is the main factor of the AES cipher system strength. The purpose of the proposed approach is to generate the random S-boxes changing for every change of the secret key. The fact that the S-boxes are randomly key-dependent and unknown is the main strength of the new approach, since both linear and differential cryptanalysis require known S-boxes. In the paper, we briefly analyze the AES algorithm, substitution S-boxes, linear and differential cryptanalysis, and describe a randomly key-dependent S-box and inverse S-box generation algorithm. After that, we introduce the independency measure of the S-box elements, and experimentally investigate the quality of the generated S-boxes.

This paper presents an iterative autoregressive system parameter estimation algorithm in the presence of white observation noise. The algorithm is based on the parameter estimation bias correction approach. We use high order Yule–Walker equations, sequentially estimate the noise variance, and exploit these estimated variances for the bias correction. The improved performance of the proposed algorithm in the presence of white noise is demonstrated via Monte Carlo experiments.

It is shown that nonlinear Volterra, polynomial autoregressive, and bilinear filters have the same layered implementation procedure. Using the layered structure, the order of nonlinearity can be increased by adding more layers to the structure. The structure is modular and consists of the simple moving average (MA) or autoregressive (AR) filters which can be added to the structure to achieve a desired degree of complexity. In addition, the modular layered structures admit very large scale integration (VLSI) implementation of the polynomial nonlinear filters.