Neural Networks are well known for their ability to selectively explore the solution space of a given problem. This feature finds a natural niche of application in the field of cryptanalysis. At the same time, Neural Networks offer a new approach to attack ciphering algorithms based on the principle that any function could be reproduced by a neural network, which is a powerful proven computational tool that can be used to find the inverse-function of any cryptographic algorithm.
The ideas of mutual learning, self learning, and stochastic behavior of neural networks and similar algorithms can be used for different aspects of cryptography, like public-key cryptography, solving the key distribution problem using neural network mutual synchronization, hashing or generation of pseudo-random numbers.
Another idea is the ability of a neural network to separate space in non-linear pieces using "bias". It gives different probabilities of activating or not the neural network. This is very useful in the case of Cryptanalysis.
Two names are used to design the same domain of researches : Neuro-Cryptography and Neural Cryptography.
The first work that it is known on this topic can be traced back to 1995 in an IT Master Thesis.
Read more about this topic: Neural Cryptography
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