Advertisement

Cryptanalysis of Four-Rounded DES Using Binary Artificial Immune System

  • Syed Ali Abbas Hamdani
  • Sarah Shafiq
  • Farrukh Aslam Khan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

In this paper, we present a new approach for the cryptanalysis of four-rounded Data Encryption Standard (DES) based on Artificial Immune System (AIS). The proposed algorithm is a combination of exploitation and exploration of fitness landscape where it performs local as well as global search. The algorithm has the property of automatically determining the population size and maintaining the local solutions in generations to generate results close to the global results. It is actually a known plaintext attack that aims at deducing optimum keys depending upon their fitness values. The set of deduced or optimum keys is scanned to extract the valuable bits out by counting all bits from the deduced key set. These valuable extracted bits produce a major divergence from other observed bits. This results in a 56-bit key deduction without probing the whole search space. To the best of our knowledge, the proposed algorithm is the first attempt to perform cryptanalysis of four-rounded DES using Artificial Immune System.

Keywords

Cryptanalysis Four-rounded DES Artificial Immune System (AIS) Fitness measure 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    de Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Function Optimization. In: Proc. of Congress on Evolutionary Computation, CEC 2002 (2002)Google Scholar
  2. 2.
    Song, J., Zhang, H., Meng, Q., Wang, Z.: Cryptanalysis of Four-Round DES Based on Genetic Algorithm. In: International Conference on Wireless Communications, Networking and Mobile Computing (WiCom 2007), Shanghai, China, pp. 2326–2329 (2007)Google Scholar
  3. 3.
    Song, J., Zhang, H., Meng, Q., Wang, Z.: Cryptanalysis of Two-Round DES Using Genetic Algorithms. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 583–590. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  5. 5.
    Coppersmith, D.: The data encryption standard (DES) and its strength against attacks. IBM Journal of Research and Development 38(3), 243–250 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Spillman, R., Janssen, M., Nelson, B., Kepner, M.: Use of A Genetic Algorithm in the Cryptanalysis of simple substitution Ciphers. Cryptologia XVII(1), 187–201 (1993)Google Scholar
  7. 7.
    Clark, A.: Modern Optimisation Algorithms for Cryptanalysis, pp. 258–262. IEEE, Los Alamitos (1994)Google Scholar
  8. 8.
    Clark, A., Dawson, E.: Optimisation Heuristics for the Automated Cryptanalysis of Classical Ciphers. J. Combinatorial Mathematics and Combinatorial Computing 28, 63–86 (1998)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Clark, A.J.: Optimization Heuristics for Cryptology, PhD thesis, Queensland University of Technology (1998)Google Scholar
  10. 10.
    Laskari, E.C., Meletiouc, G.C., Stamatioud, Y.C., Vrahatis, M.N.: Evolutionary computation based cryptanalysis: A first study, pp. 823–830. Elsevier, Amsterdam (2005)Google Scholar
  11. 11.
    Hernández, J.C., et al.: Easing collision finding in cryptographic primitives with genetic algorithms. In: Proc. of CEC 2002, Honolulu, HI, USA, vol. 1, pp. 535–539 (2002)Google Scholar
  12. 12.
    Russell, M., Clark, J.A., Stepney, S.: Using Ants to Attack a Classical Cipher. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 146–147. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Morelli, R., Walde, R., Servos, W.: A Study of Heuristic Approaches for Breaking short Cryptograms. International Journal on Artificial Intelligence Tools 13(1), 45–64 (2004)CrossRefGoogle Scholar
  14. 14.
    Bafghi, A.G., Sadeghiyan, B.: Finding Suitable Differential Characteristics for Block Ciphers with Ant Colony Technique. In: Proc. of Ninth International Symposium on Computers and Communications (ISCC 2004), Washington, DC, USA, vol. 2, pp. 418–423 (2004)Google Scholar
  15. 15.
    Clark, J.A., Jacob, J.L., Stepney, S.: The Design of S-Boxes by Simulated Annealing. New Generation Computing 23(3), 219–231 (2005)zbMATHCrossRefGoogle Scholar
  16. 16.
    Castro, J.C.H., Sierra, J.M., Isasi, P., Ribagorda, A.: Genetic Cryptoanalysis of Two Rounds TEA. In: Sloot, P.M.A., Tan, C.J.K., Dongarra, J., Hoekstra, A.G. (eds.) ICCS-ComputSci 2002. LNCS, vol. 2331, pp. 1024–1031. Springer, Heidelberg (2002)Google Scholar
  17. 17.
    Matsui, M.: The First Experimental Cryptanalysis of the Data Encryption Standard. In: Desmedt, Y.G. (ed.) CRYPTO 1994. LNCS, vol. 839, pp. 1–11. Springer, Heidelberg (1994)Google Scholar
  18. 18.
    Shahzad, W., Siddiqui, A.B., Khan, F.A.: Cryptanalysis of Four-Rounded DES using Binary Particle Swarm Optimization. In: ACM GECCO 2009, Montréal, Québec, Canada, pp. 2161–2166 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Syed Ali Abbas Hamdani
    • 1
  • Sarah Shafiq
    • 1
  • Farrukh Aslam Khan
    • 1
  1. 1.Department of Computer ScienceFAST National University of Computer and Emerging SciencesIslamabadPakistan

Personalised recommendations