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Genetic attack on neural cryptography
2006
Physical Review E
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
doi:10.1103/physreve.73.036121
pmid:16605612
fatcat:z4uqql5aqnegrdb5mi7cnolpcy