Combining RBF neural network and chaotic map to construct hash function

  • Authors:

  • Pengcheng Wei;Wei Zhang;Huaqian Yang;Jun Chen

  • Affiliations:

  • Department of Computer Science and Engineering, Chongqing University, China;Department of Computer Science and Engineering, Chongqing University, China;Department of Computer Science and Engineering, Chongqing University, China;Department of Computer and Modern Education Technology, Chongqing Education College, Chongqing, China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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Abstract

How to design an efficient and security keyed hash function is always the point in modern cryptography researches. In this paper, A better chaos sequence is generated by RBF neural network through training the known chaotic sequence generated by a piecewise nonlinear map, then the sequence is used to construct keyed hash function. One advantage of the algorithm is that the hidden-mapping model of neural network makes it difficult to get the direct mapping function of the ordinary chaos hash algorithm. Simulation results show that the keyed hash function based on the neural network has good one-way, weak collision, better security property and it can be realized easily.