Genetic attack on neural cryptography

Abstract

Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

8 pages, 12 figures; section 5 amended, typos corrected

References in corpus (6)

• Secure exchange of information by synchronization of neural networks
• Public channel cryptography by synchronization of neural networks and chaotic maps
• A secure key-exchange protocol with an absence of injective functions
• Cooperating Attackers in Neural Cryptography
• Neural cryptography with feedback
• Synchronization of random walks with reflecting boundaries

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• Neural Synchronization and Cryptography
• Key Generation and Certification using Multilayer Perceptron in Wireless communication(KGCMLP)
• Successful attack on permutation-parity-machine-based neural cryptography
• Chaos Synchronization with Dynamic Filters: Two Way is Better Than One Way
• Multilayer Perceptron Guided Key Generation Through Mutation with Recursive Replacement in Wireless Communication (MLPKG)