Applied cryptography (2nd ed.): protocols, algorithms, and source code in C
Applied cryptography (2nd ed.): protocols, algorithms, and source code in C
Handbook of Applied Cryptography
Handbook of Applied Cryptography
Statistical Mechanics of Learning
Statistical Mechanics of Learning
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Cryptography and Network Security: Principles and Practice
Cryptography and Network Security: Principles and Practice
Analysis of Neural Cryptography
ASIACRYPT '02 Proceedings of the 8th International Conference on the Theory and Application of Cryptology and Information Security: Advances in Cryptology
Neural Network Theory
Genetic Systems Programming: Theory and Experiences
Genetic Systems Programming: Theory and Experiences
Decision Manifolds—A Supervised Learning Algorithm Based on Self-Organization
IEEE Transactions on Neural Networks
An approach for designing neural cryptography
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Neural cryptography deals with the problem of "key exchange" between two neural networks using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between the two communicating parties is eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process. Therefore, diminishing the probability of such a threat improves the reliability of exchanging the output bits through a public channel. The synchronization with feedback algorithm is one of the existing algorithms that enhances the security of neural cryptography. This paper proposes three new algorithms to enhance the mutual learning process. They mainly depend on disrupting the attacker confidence in the exchanged outputs and input patterns during training. The first algorithm is called "Do not Trust My Partner" (DTMP), which relies on one party sending erroneous output bits, with the other party being capable of predicting and correcting this error. The second algorithm is called "Synchronization with Common Secret Feedback" (SCSFB), where inputs are kept partially secret and the attacker has to train its network on input patterns that are different from the training sets used by the communicating parties. The third algorithm is a hybrid technique combining the features of the DTMP and SCSFB. The proposed approaches are shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize.