Abstract
Feistel cipher based cryptographic algorithms like DES are very hard for the cryptanalysts as their internal structures are based on high nonlinearity and low autocorrelation. It has been shown that the traditional and brute-force type attacks are insignificant for the cryptanalysis of this type of algorithms. Swarm intelligence is an exciting new research field and shown their effectiveness, robustness to solve a wide variety of complex problems. Therefore, in this paper, Binary Particle Swarm Optimization (BPSO) strategy is used for cryptanalysis of DES symmetric key cryptographic algorithm. The reported results show that it is very promising to solve block cipher based cryptographic optimization problem through meta heuristic techniques.
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References
Arumugam, M.S., Rao, M.V.C., Tan, A.W.C.: A novel and effective particle swarm optimization like algorithm with extrapolation technique. Applied Soft Computing 9(1), 308–320 (2009)
Biham, E., Shamir, A.: Differential cryptanalysis of des-like cryptosystems. Journal of CRYPTOLOGY 4(1), 3–72 (1991)
Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 3. IEEE (1999)
Thakur, M., Deep, K.: A new crossover operator for real coded genetic algorithms. Applied Mathematics and Computation 188(1), 895–911 (2007)
Diffie, W., Hellman, M.E.: Special feature exhaustive cryptanalysis of the nbs data encryption standard. Computer 10(6), 74–84 (1977)
Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 2. IEEE (1999)
Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 84–88. IEEE (2000)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, ’Computational Cybernetics and Simulation’, vol. 5, pp. 4104–4108. IEEE (1997)
Laskari, E.C., Meletiou, G.C., Stamatiou, Y.C., Vrahatis, M.N.: Evolutionary computation based cryptanalysis: A first study. Nonlinear Analysis 63(5-7), e823–e830 (2005)
Passino, K.M.: Bacterial foraging optimization. International Journal of Swarm Intelligence Research (IJSIR) 1(1), 1–16 (2010)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential evolution: a practical approach to global optimization. Springer, Heidelberg (2005)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE (1998)
Shi, Y., Eberhart, R.: Parameter Selection in Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)
Williamson, D.F., Parker, R.A., Kendrick, J.S.: The box plot: a simple visual method to interpret data. Annals of Internal Medicine 110(11), 916 (1989)
Yagmahan, B., Yenisey, M.M.: Ant colony optimization for multi-objective flow shop scheduling problem. Computers & Industrial Engineering 54(3), 411–420 (2008)
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Jadon, S.S., Sharma, H., Kumar, E., Bansal, J.C. (2012). Application of Binary Particle Swarm Optimization in Cryptanalysis of DES. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_97
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