Neural cryptography with feedback

Abstract

Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

8 pages, 10 figures; abstract changed, references updated

References in corpus (6)

• Secure exchange of information by synchronization of neural networks
• Public channel cryptography by synchronization of neural networks and chaotic maps
• Mutual learning in a tree parity machine and its application to cryptography
• A secure key-exchange protocol with an absence of injective functions
• Cryptography based on neural networks - analytical results
• Generation of unpredictable time series by a Neural Network

Cited by in corpus (9)

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• Dynamics of neural cryptography
• Comment on novel public key encryption technique based on multiple chaotic systems
• Neural cryptography with queries
• Synchronization of random walks with reflecting boundaries
• Neural Synchronization and Cryptography
• Successful attack on permutation-parity-machine-based neural cryptography
• Authenticated tree parity machine key exchange
• Tree Parity Machine Rekeying Architectures