This paper deals with the theory and application of Cellular Automata (CAI for a class of block ciphers and stream ciphers. Based on CA state transitions certain fundamental transformations are defined which are block ciphering functions of the proposed enciphering scheme. These fundamental transformations are found to generate the simple (alternating) group of even permutations which in turn is a subgroup of the permutation group. These functions are implemented with a class of programmable cellular automata (PCA) built around rules 51, 153, and 195. Further, high quality pseudorandom pattern generators built around rule 90 and 150 programmable cellular automata with a rule selector (Le., combining function) has been proposed as running key generators in stream ciphers. Both the schemes provide better security against different types of attacks. With a simple, regular, modular and cascadable structure of CA, hardware implementation of such schemes idealy suit for VLSI implementation.

No abstract

No abstract

This paper reports an efficient technique of evolving Cellular Automata (CA) as an associative memory model. The evolved CA termed as GMACA (Generalized Multiple Attractor Cellular Automata), acts as a powerful pattern recognizer. Detailed analysis of GMACA rules establishes the fact that the rule subspace of the pattern recognizing CA lies at the edge of chaos — believed to be capable of executing complex computation.

No abstract

The cipher systems based on Cellular Automata proposed by Nandi et al. 3] are a ne and are insecure.

This paper reports a cellular automata (CA) based model of associative memory. The model has been evolved around a special class of CA referred to as generalized multiple attractor cellular automata (GMACA). The GMACA based associative memory is designed to address the problem of pattern recognition. Its storage capacity is found to be better than that of Hopfield network. The GMACA are configured with nonlinear CA rules that are evolved through genetic algorithm (GA). Successive generations of GA select the rules at the edge of chaos. The study confirms the potential of GMACA to perform complex computations like pattern recognition at the edge of chaos.

No abstract