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


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.


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


  1. 1.
    Vidali J, Nose P, Pas̆alić E (2010) Collisions for variants of the BLAKE hash function. Inf Process Lett 110:585–590zbMATHCrossRefGoogle Scholar
  2. 2.
    Wang S, Li D, Zhou H (2012) Collision analysis of a chaos-based hash function with both modification detection and localization capability. Commun Nonlinear Sci Numer Simulat 17:780–784MathSciNetCrossRefGoogle Scholar
  3. 3.
    Wang X, Zhao J (2010) Cryptanalysis on a parallel keyed hash function based on chaotic neural network. Neurocomputing 73:3224–3228CrossRefGoogle Scholar
  4. 4.
    Xingyuan W, Guoxiang H (2011) Cryptanalysis on a novel image encryption method based on total shuffling scheme. Optics Commun 284(24):5804–5807CrossRefGoogle Scholar
  5. 5.
    Xiao D, Liao X, Deng S (2008) Parallel keyed hash function construction based on chaotic maps. Phys Lett A 372:4682–4688MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Wang X, He D, Cao Y (2009) Cryptanalysis on a parallel keyed hash function based on chaotic maps. Phys Lett A 373:3201–3206MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Xiao D, Liao X, Wang Y (2009) Parallel keyed hash function construction based on chaotic neural network. Neurocomputing 72:2288–2296CrossRefGoogle Scholar
  8. 8.
    Laskari EC, Meletiou GC, Stamation YC, Vrahatis MN (2005) Evolutionary Computation based Cryptanalysis: A first study. Nonlinear Anal 63:823–830MathSciNetCrossRefGoogle Scholar
  9. 9.
    Xiao D, Li Y, Deng S (2011) Parallel Hash function construction based on chaotic maps with changeable parameters. Neural Comput Appl 20:1305–13012CrossRefGoogle Scholar
  10. 10.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks IV:1942–1948Google Scholar
  11. 11.
    Shahzad W, Siddiqui AB, Khan FA (2009) Cryptanalysis of Four-Round DES using Binary Particle Swarm Optimization. Genetic and Evolutionary Computation Conference \((GECCO)\), pp 1757–1758Google Scholar
  12. 12.
    Ghonaim WA, Ghali NI, Hassanien AE, Abraham A (2011) Known-plaintext attack of DES-16 using Particle Swarm Optimization. Nature and Biologically Inspired Computing \((NaBIC)\), pp 12–16Google Scholar
  13. 13.
    Abraham A (2005) Artificial Neural Networks. Handbook of Measuring System DesignGoogle Scholar
  14. 14.
    Cathy W (1997) Artificial neural networks for molecular sequence analysis. Comput Chem 21:237–256CrossRefGoogle Scholar
  15. 15.
    Wang Y, Wong KW, Xiao D (2011) Parallel hash function construction based on coupled map lattices. Commun Nonlinear Sci Numer Simulat 16:2810–2821MathSciNetzbMATHCrossRefGoogle Scholar

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

Personalised recommendations