Neural cryptography: Difference between revisions

Revision as of 11:54, 19 February 2019

Revision as of 19:23, 15 March 2019

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There are currently no practical applications due to the recent development of the field, but it could be used specifically where the keys are continually generated and the system (both pairs and the insecure media) is in a continuously evolving mode.<br />

In 1995, Sebastien Dourlens applied neural networks to cryptanalyze [[Data Encryption Standard|DES]] by allowing the networks to learn how to invert the S-tables of the DES. The bias in DES studied through Differential Cryptanalysis by [[Adi Shamir]] is highlighted. The experiment shows about 50% of the key bits can be found, allowing the complete key to be found in a short time. Hardware application with multi micro-controllers have been proposed due to the eeasy he tiygvg ugh ru gu mplementationimplementation of multilayer neural networks in hardware.<br />

One example of a public-key protocol is given by Khalil Shihab. He describes the decryption scheme and the public key creation that are based on a [[backpropagation]] neural network. The encryption scheme and the private key creation process are based on Boolean algebra. This technique has the advantage of small time and memory complexities. A disadvantage is the property of backpropagation algorithms: because of huge training sets, the learning phase of a neural network is very long. Therefore, the use of this protocol is only theoretical so far.