  This thesis aims to use intelligent systems to extend and improve performance
and security of cryptographic techniques. Genetic algorithms framework for
cryptanalysis problem is addressed. A novel extension to the differential
cryptanalysis using genetic algorithm is proposed and a fitness measure based
on the differential characteristics of the cipher being attacked is also
proposed. The complexity of the proposed attack is shown to be less than
quarter of normal differential cryptanalysis of the same cipher by applying the
proposed attack to both the basic Substitution Permutation Network and the
Feistel Network. The basic models of modern block ciphers are attacked instead
of actual cipher to prove that the attack is applicable to other ciphers
vulnerable to differential cryptanalysis. A new attack for block cipher based
on the ability of neural networks to perform an approximation of mapping is
proposed. A complete problem formulation is explained and implementation of the
attack on some hypothetical Feistel cipher not vulnerable to differential or
linear attacks is presented. A new block cipher based on the neural networks is
proposed. A complete cipher structure is given and a key scheduling is also
shown. The main properties of neural network being able to perform mapping
between large dimension domains in a very fast and a very small memory compared
to S-Boxes is used as a base for the cipher.
