SECURITY ENHANCEMENT USING NEURAL NETWORKS FOR DATA TRANSMISSION

Abstract

Cryptography is a process of protecting information and data from unauthorized access. The goal of any cryptographic system is the access of information among the intended user without any leakage of information. As technology advances the methods of traditional cryptography becomes more and more complex. Neural Networks provide an alternate way for crafting a security system whose complexity is far less than that of the current cryptographic systems and has been proven to be productive. Both the communicating neural networks receive an identical input vector, generate an output bit and are based on the output bit. Our approach is based on the application of natural noise sources that we can use to teach our system to approximate with the aim of generating an output non-linear function. This approach provides the potential for generating an unlimited number of unique Pseudo Random Number Generator (PRNG) that can be used on a one to one basis. We can use the PRNG generated to encrypt the input data and create a cipher text that has a high cryptographic strength.