Improving pseudorandom bit sequence generation and evaluation for secure Internet communications using neural network techniques

Random components play an especially important role in secure electronic commerce and Internet communications. 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 architectures, to strengthen traditional generators such as ANSI X.9 based DES and IDEA. Additionally, this paper proposes a test method for evaluating the required non-predictability property, which also relies on neural networks. This non-predictability test method along with commonly used statistical and non-linearity tests are proposed as methodology for the evaluation of strong pseudo random number generators. By means of this methodology, traditional and proposed generators are evaluated. The results show that the proposed generators behave significantly better than the traditional, in particular, in terms of non-predictability.