Analysis and design of stream ciphers
Analysis and design of stream ciphers
Multilayer feedforward networks are universal approximators
Neural Networks
Pseudorandom Bit Generators in Stream-Cipher Cryptography
Computer - Special issue on cryptography
Authentication services in distributed systems
Computers and Security
Network and internetwork security: principles and practice
Network and internetwork security: principles and practice
A computer package for measuring the strength of encryption algorithms
Computers and Security
Security in computing
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Cryptography: Theory and Practice
Cryptography: Theory and Practice
Contemporary Cryptology: The Science of Information Integrity
Contemporary Cryptology: The Science of Information Integrity
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Secure Media Distribution Scheme Based on Chaotic Neural Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Letters: A block cipher based on chaotic neural networks
Neurocomputing
Traceable content protection based on chaos and neural networks
Applied Soft Computing
Can dynamic neural filters produce pseudo-random sequences?
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
A novel image encryption/decryption scheme based on chaotic neural networks
Engineering Applications of Artificial Intelligence
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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.