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Hash function based on efficient chaotic neural network

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dc.contributor.author Abdoun, Nabil
dc.contributor.author El Assad, Safwan
dc.contributor.author Abutaha, Mohammed
dc.contributor.author Assaf, Rima
dc.contributor.author Deforges, Olivier
dc.contributor.author Khalil, Mohamad
dc.date.accessioned 2020-12-09T09:22:53Z
dc.date.accessioned 2022-05-22T08:52:23Z
dc.date.available 2020-12-09T09:22:53Z
dc.date.available 2022-05-22T08:52:23Z
dc.date.issued 2015
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/8099
dc.description.abstract This paper presents an efficient algorithm for constructing a secure Hash function based on Chaotic Neural Network structure. The proposed Hash function includes two main operations: Generation of Neural Network parameters using fast and efficient Chaotic Generator and Iteration of the message through the Chaotic Neural Network. Our theoretical analysis and experimental simulations showed that the implemented Hash function has good statistical properties, strong Collision Resistance and High Message Sensitivity.
dc.publisher IEEE
dc.title Hash function based on efficient chaotic neural network

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