Generating High Quality Pseudo Random Number Using Evolutionary methods

Abstract

Random number generators represent basic cryptographic primitives. They are widely used in modern security schemes including security protocols, computer security and password sources. In some algorithms (e.g. DSA) or protocols (e.g. zero-knowledge), random numbers are intrinsic to the computation [3]. In all these applications, strength of security depends greatly on the quality of randomness of the source. Pseudo random number generators (PRNGs) play an important role in cryptographic applications, as the security of these systems depends on the assumption that future values in the random sequence can be unpredictable. A linear feedback shift register (LFSR), in effect, implements a binary polynomial generator. These generators find common use for PRNGs. However, the random sequence generated by LFSR can not guarantee the unpredictability in secure system. We have used genetic algorithm to improve LFSR and proposed a novel random number generator with complex architecture and longer period.