Optimal Rules Identification for a Random Number Generator Using Cellular Learning Automata

The cryptography is known as one of most essential ways for protecting information against threats. Among all encryption algorithms, stream ciphering can be indicated as a sample of swift ways for this purpose, in which, a generator is applied to produce a sequence of bits as the key stream. Although this sequence is seems to be random, severely, it contains a pattern that repeats periodically. Linear Feedback Shift Registers and cellular automata have been used as pseudo-random number generator. Some challenges such as error propagation and pattern dependability have motivated the designers to use CA for this purpose. The most important issue in using cellular automata includes determining an optimal set of rules for cells. This paper focuses on selecting optimal rules set for such this generator with using an open cellular learning automata, which is a cellular automata with learning capability and interacts with local and global environments

Synthesis of Cryptographic Interleaved Sequences by means of Linear Cellular Automata

This work shows that a class of pseudorandom binary sequences, the so-called interleaved sequences, can be generated by means of linear multiplicative polynomial cellular automata. In fact, these linear automata generate all the solutions of a type of linear difference equations with binary coefficients. Interleaved sequences are just particular solutions of such equations. In this way, popular nonlinear sequence generators with cryptographic application can be linearized in terms of simple cellular automata.This work has been developed in the frame of the project HESPERIA under programme CENIT and supported by CDTI as well as by the companies: Soluziona, Unión Fenosa, Tecnobit, Visual-Tools, BrainStorm, SAC and TechnoSafe.Peer reviewe