Design and Implementation of Stream Cipher Using Neural Network

Siddeq Ameen,Mazin Othman,Safwan Hasoon,Moyed Al-Razaq

doi:10.33899/csmj.2009.163781

Siddeq Ameen, Mazin Othman + Show 2 more

Open Access

https://doi.org/10.33899/csmj.2009.163781

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Abstract

The centaral problem in stream cipher cryptograph is the the difficulty to generate a long unpredicatable sequence of binary signals from short and random key. Unpredicatable sequence are desirable in cryptography because it is impossible, given a reasonable segment of its signals and computer resources, to find out more about them. Pseudorandom bit generators have been widely used to construct these sequences. The paper presents a PN sequence generator that uses neural network. Computer simulation tests have been carried out to check the randomness of the generated through statistical tests. There tests have shown the successful PN sequence generator passes all the recommended tests. The paper also proposes and validates the data encryption and decryption process using neural network instead of using traditional methods (Exclusive or). This task increases the difficulty in the breaking the cipher.

Highlights

Stream ciphers can be designed to be exceptionally fast, much faster than any block cipher
If a block cipher were to be used in this type of application, considerable bandwidth would end up being wasted by padding, since block ciphers cannot work on blocks shorter than their block size
Artificial neural networks (ANNs) are highly parallel interconnections of simple processing elements or neurons that function as a collective system

Summary

Introduction

Stream ciphers can be designed to be exceptionally fast, much faster than any block cipher. Artificial neural networks (ANNs) are highly parallel interconnections of simple processing elements or neurons that function as a collective system. Artificial neural network consists of a large number of simple processing elements (PEs) densely interconnected, analogous to neurons of human brain. The neural network is made up of several layers of processing elements connected together via unidirectional signal channels associated with weights, analogous to synapses. When the error is found, the error is propagate back to the input layer for weights adjusting(third phase) This process is repeated until the desired output obtained. 2.Sums weighted input and apply activition function to compute the output of the hidden layer using: n hi = f ( PiWij + bj ) i =1.

Update weights and biases using

Calculate weight and key weight correction output layer using:

Conclusions