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Lecture notes in computer sciences; 218 on Advances in cryptology---CRYPTO 85
The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Products of linear recurring sequences with maximum complexity
IEEE Transactions on Information Theory
Modern cryptology
Cryptography: an introduction to computer security
Cryptography: an introduction to computer security
On the security of multiple encryption
Communications of the ACM
A method for obtaining digital signatures and public-key cryptosystems
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
On the security of a clipped hopfield neural network-based cryptosystem
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
A Symmetric Probabilistic Encryption Scheme Based On CHNN Without Data Expansion
Neural Processing Letters
Digital video encryption algorithms based on correlation-preserving permutations
EURASIP Journal on Information Security
Security Analysis of Public-Key Encryption Scheme Based on Neural Networks and Its Implementing
Computational Intelligence and Security
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
Improvement of an Image Encryption Algorithm Based on Combined Multidimensional Chaotic Systems
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Letters: A block cipher based on chaotic neural networks
Neurocomputing
Traceable content protection based on chaos and neural networks
Applied Soft Computing
A chaotic neural network-based encryption algorithm for MPEG-2 encoded video signal
International Journal of Artificial Intelligence and Soft Computing
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A new probabilistic symmetric-key encryption scheme based on chaotic-classified properties of Hopfield neural networks is described. In an overstoraged Hopfield Neural Network (OHNN) the phenomenon of chaotic-attractors is well documented and messages in the attraction domain of an attractor are unpredictably related to each other. By performing permutation operations on the neural synaptic matrix, several interesting chaotic-classified properties of OHNN were found and these were exploited in developing a new cryptography technique. By keeping the permutation operation of the neural synaptic matrix as the secret key, we introduce a new probabilistic encryption scheme for a symmetric-key cryptosystem. Security and encryption efficiency of the new scheme are discussed.