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Improvement and performance analysis of a novel hash function based on chaotic neural network

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Abstract

In this paper, we reconsider and analyze our previous paper a novel hash algorithm construction based on chaotic neural network, then present equal-length and unequal-length forgery attacks against its security in detail, and then propose a significantly improved approach by utilizing a method of complicated nonlinear computation to enhance the security of the original hash algorithm. Theoretical analysis and computer simulation indicate that the improved algorithm can completely resist the two kinds of forgery attacks and also shows other better performance than the original one, such as better message and key sensitivity, statistical properties, which can satisfy the performance requirements of a more secure hash function.

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References

  1. Wong KW (2003) A combined chaotic cryptographic and hashing scheme. Phys Lett A 307:292–298

    Article  MathSciNet  MATH  Google Scholar 

  2. Kwok HS, Tang WKS (2005) A chaos-based cryptographic Hash function for message authentication. Int J Bifur Chaos 15:4043–4050

    Article  MATH  Google Scholar 

  3. Xiao D, Liao XF, Deng SJ (2005) One-way Hash function construction based on the chaotic map with changeable-parameter. Chaos Solitons Fractals 24:65–71

    MathSciNet  MATH  Google Scholar 

  4. Zhang JS, Wang XM, Zhang WF (2007) Chaotic keyed Hash function based on feedforward–feedback nonlinear digital filter. Phys Lett A 362:439–448

    Article  MATH  Google Scholar 

  5. Wang Y, Liao XF, Xiao D, Wong KW (2008) One-way hash function construction based on 2D coupled map lattices. Inform Sci 178:1391–1406

    Article  MATH  Google Scholar 

  6. Xiao D, Liao XF, Deng SJ (2008) Parallel keyed hash function construction based on chaotic maps. Phys Lett A 372:4682–4688

    Article  MathSciNet  MATH  Google Scholar 

  7. Deng SJ, Li YT, Xiao D (2009) Analysis and improvement of a chaos-based hash function construction. Commun Nonlinear Sci Numer Simulat 15:1338–1347

    Article  MathSciNet  Google Scholar 

  8. Yang HQ, Wong KW, Liao XF et al (2009) One-way hash function construction based on chaotic map network. Chaos Solitons Fractals 41:2566–2574

    Article  Google Scholar 

  9. Akhshani A, Behnia S, Akhavan A et al (2009) Hash function based on hierarchy of 2D piecewise nonlinear chaotic maps. Chaos Solitons Fractals 42:2405–2412

    Article  MathSciNet  Google Scholar 

  10. Xiao D, Shih FY, Liao XF (2010) A chaos-based hash function with both modification detection and localization capabilities. Commun Nonlinear Sci Numer Simulat 15:2254–2261

    Article  MathSciNet  MATH  Google Scholar 

  11. Wang Y, Wong KW, Xiao D (2011) Parallel hash function construction based on coupled map lattices. Commun Nonlinear Sci Numer Simulat 16:2810–2821

    Article  MathSciNet  MATH  Google Scholar 

  12. Lian SG, Sun JS, Wang ZQ (2006) Secure hash function based on neural network. Neurocomputing 69:2346–2350

    Article  Google Scholar 

  13. Liu GL, Shan L, Dai YW et al (2006) One-way hash function based on chaotic neural network. Acta Phys Sin 55:5688–5706 (in Chinese)

    MathSciNet  MATH  Google Scholar 

  14. Xiao D, Liao XF, Wang Y (2009) Parallel keyed hash function construction based on chaotic neural network. Neurocomputing 72:2288–2296

    Article  Google Scholar 

  15. Li YT, Deng SJ, Xiao D (2011) A novel Hash algorithm construction based on chaotic neural network. Neural Comput Applic 20:133–141

    Article  Google Scholar 

  16. Guo W, Wang XM, He DK et al (2009) Cryptanalysis on a parallel keyed hash function based on chaotic maps. Phys Lett A 373:3201–3206

    Article  MathSciNet  MATH  Google Scholar 

  17. Xiao D, Peng WB, Liao XF et al (2010) Collision analysis of one kind of chaos-based hash function. Phys Lett A 374:1228–1231

    Article  MATH  Google Scholar 

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Acknowledgments

Our sincere thanks go to the anonymous reviewers for their valuable comments. The work described in this paper was fully funded by Project No. CDJZR10180003 supported by the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Yantao Li, Di Xiao & Shaojiang Deng

  2. Department of Computer Science, College of William and Mary, Williamsburg, VA, 23185, USA

    Yantao Li & Gang Zhou

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  1. Yantao Li
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  2. Di Xiao
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  3. Shaojiang Deng
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  4. Gang Zhou
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Correspondence to Yantao Li.

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Li, Y., Xiao, D., Deng, S. et al. Improvement and performance analysis of a novel hash function based on chaotic neural network. Neural Comput & Applic 22, 391–402 (2013). https://doi.org/10.1007/s00521-011-0703-6

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  • DOI: https://doi.org/10.1007/s00521-011-0703-6

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