Using Fuzzy ARTMAP for Symmetric Key Generation

dc.contributor.authorMulopa, John
dc.contributor.authorNdlovu, Siqabukile
dc.contributor.authorMzelikahle, Kernan
dc.contributor.authorNyathi, Thambo
dc.date.accessioned2017-11-07T14:33:35Z
dc.date.accessioned2023-06-26T13:07:16Z
dc.date.available2017-11-07T14:33:35Z
dc.date.available2023-06-26T13:07:16Z
dc.date.issued2015
dc.descriptionJournal Articleen_US
dc.description.abstractNeural cryptography deals with the problem of key exchange between two communicating neural networks using the mutual learning concept. It is the first algorithm for key generation over public channels which are not based on the number theory. The two networks exchange their outputs and the key between the two communicating parties is eventually presented in the final learned weights, when the two networks are synchronised. The security of neural synchronisation is put at risk if an attacker is capable of synchronising with any of the two parties during the training processes. However, the security of a cryptosystem is robust if the algorithm is strong and the keys are long, unpredictable, and random This research proposes use of two distant remote Adaptive Resonance Theory MAP (ARTMAP) architectures that are trained to learn from a unique data set and finally synchronise to same weights.en_US
dc.identifier.citationMulopa J. Ndlovu S. Mzelikahle K. Nyathi T. 2015. Using Fuzzy ARTMAP for Symmetric Key Generation : American Journal of Information Science and Computer Engineering 1(2)en_US
dc.identifier.urihttp://196.220.97.103:4000/handle/123456789/840
dc.language.isoenen_US
dc.publisherAmerican Institute of Science.en_US
dc.subjectNeural Networksen_US
dc.subjectCryptographyen_US
dc.subjectCryptosystemen_US
dc.subjectSynchronisationen_US
dc.subjectKey Generationen_US
dc.subjectArtmapen_US
dc.titleUsing Fuzzy ARTMAP for Symmetric Key Generationen_US
dc.typeArticleen_US
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