Random components play an especially important role in secure electronic commerce and multimedia 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 on 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 nonlinearity tests are suggested 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 nonpredictability.
['Dimitris A. Karras', 'Vasilios Zorkadis']
Strong Pseudorandom Bit Sequence Generators Using Neural Network Techniques and Their Evaluation for Secure Communications