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Memetic Computing

, Volume 5, Issue 3, pp 179–185 | Cite as

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

  • W. Ghonaim
  • Neveen I. Ghali
  • Aboul Ella HassanienEmail author
  • Soumya Banerjee
Regular research paper

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.

Keywords

Artificial neural networks Particle swarm optimization  Hamming distance Semi-collision attack Chaotic neural network 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • W. Ghonaim
    • 1
  • Neveen I. Ghali
    • 1
  • Aboul Ella Hassanien
    • 2
    Email author
  • Soumya Banerjee
    • 3
  1. 1.Faculty of ScienceAl-Azhar UniversityCairoEgypt
  2. 2.Information Technology DepartmentFCI, Cairo UniversityGizahEgypt
  3. 3.Birla Institute of TechnologyMesraIndia

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