An Efficient Security System Using Neural Networks
Journal Title: International Journal of Engineering and Science Invention - Year 2018, Vol 7, Issue 1
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 to others who may have unauthorized access to it. As technology advances the methods of traditional cryptography becomes more and more complex. There is a high rate of exhaustion of resources while maintaining this type of traditional security systems. 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. The two networks and their weight vectors exhibit novel phenomenon, where the networks synchronize to a state with identical time-dependent weights. Our approach is based on the application of natural noise sources that can include atmospheric noise generating data that we can use to teach our system to approximate the input noise 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.
Authors and Affiliations
Amit Kore, Preeyonuj Boruah, Kartik Damania, Vaidehi Deshmukh, Sneha Jadhav
Keywords
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