On neural network techniques in the secure management of communication systems through improving and quality assessing pseudorandom stream generators

Random components play an especially important role in the management of secure communication systems, with emphasis on the key management of cryptographic protocols. For this reason, the existence of strong pseudo random number generators is highly required. This paper presents novel techniques, which rely on Artificial Neural Network (ANN) architectures, to strengthen traditional generators such as IDEA and ANSI X.9 based on 3DES and IDEA. Additionally, this paper proposes a non-linear test method for the quality assessment of the required non-predictability property, which relies on feedforward neural networks. This non-predictability test method along with commonly used empirical tests based on statistics is proposed as a methodology for quality assessing strong pseudorandom stream generators. By means of this methodology, traditional and Neural Network based pseudorandom stream generators are evaluated. The results show that the proposed generators behave significantly better than the traditional ones, in particular, in terms of non-predictability.