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An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack

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Abstract

In this paper, the chaos-based hash function is analyzed, then an improved version of chaos-based hash function is presented and discussed using chaotic neural networks. It is based on the piecewise linear chaotic map that is used as a transfer function in the input and output of the neural network layer. The security of the improved hash function is also discussed and a novel type of collision resistant hash function called semi-collision attack is proposed, which is based on the collision percentage between the two hash values. In the proposed attack particle swarm optimization algorithm is used to define the fitness function parameters. Finally, numerical and simulation results provides strong collision resistance and high performance efficiency.

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Authors and Affiliations

  1. Faculty of Science, Al-Azhar University, Cairo, Egypt

    W. Ghonaim & Neveen I. Ghali

  2. Information Technology Department, FCI, Cairo University, Gizah, Egypt

    Aboul Ella Hassanien

  3. Birla Institute of Technology, Mesra, India

    Soumya Banerjee

Authors
  1. W. Ghonaim
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  2. Neveen I. Ghali
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  3. Aboul Ella Hassanien
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  4. Soumya Banerjee
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Corresponding author

Correspondence to Aboul Ella Hassanien.

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Ghonaim, W., Ghali, N.I., Hassanien, A.E. et al. An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack. Memetic Comp. 5, 179–185 (2013). https://doi.org/10.1007/s12293-013-0113-7

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  • DOI: https://doi.org/10.1007/s12293-013-0113-7

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