Abstract
Chaotic map gained its importance in the field of cryptography, due to its properties like, randomness, unpredictability, sensitivity on initial condition, aperiodicity, which is used to generate pseudorandom bit streams. In this paper the optimal values of chaos parameters are generated through Real Coded Genetic Algorithm (RCGA), which is optimal, unpredictable, and optimally sensitive. Here, a non-deterministic RCGA based optimal pseudo-random bit sequence generator based on Chaotic maps, such as Logistic Chaotic map, Skew Tent Chaotic map, Cross Coupled Logistic Chaotic map, Cross Coupled Skew Tent Chaotic map is proposed. A real coded crossover and mutation technique is proposed for RCGA. Seed values for chaotic map have been optimized by using all sub-functions of GA (RCGA). These seed values are used to generate optimal pseudorandom bit stream of finite length. The randomness of the bit stream is tested by using NIST statistical test suit.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Menezes, A., van Oorschot, P., Vanstone, S.: Handbook of Applied Cryptography. CRC Press, Boca Raton (1996)
Akhshani, A., Akhavan, A., Mobaraki, A., Lim, S.C., Hassan, Z.: Pseudo random number generator based on quantum chaotic map. Commun. Nonlinear Sci. Numer. Simul. 19(1), 101–111 (2014)
Bandyopadhyay, D., et al.: A novel secure image steganography method based on chaos theory in spatial domain. Int. J. Secur. Priv. Trust Manage. (IJSPTM) 3(1), 11–22 (2014)
Bassham, L.E., et al.: SP 800–22 rev. 1a. A statistical test suite for random and pseudorandom number generators for cryptographic applications. National Institute of Standards & Technology, April 2010
Bhoskar, T., et al.: Genetic algorithm and its applications to mechanical engineering: a review. In: 4th International Conference on Materials Processing and Characterization, vol. 2, no. (4–5), pp. 2624–2630, July 2015
Barangi, M., Chang, J.S., Mazumder, P.: Straintronics-based true random number generator for high speed and energy-limited applications. IEEE Trans. Magn. 52(1), 1–9 (2016)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Found. Genet. Algorithms 1, 69–93 (1991)
Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison- Wesley, Boston (1989)
Garca-Martnez, M., Campos-Cantn, E.: Pseudo-random bit generator based on multi-modal maps. Nonlinear Dyn. 82(4), 2119–2131 (2015)
Hu, H., Liu, L., Ding, N.: Pseudorandom sequence generator based on the chen chaotic system. Comput. Phys. Commun. 184(3), 765–768 (2013)
Liu, L., Miao, S., Cheng, M., Gao, X.: A pseudorandom bit generator based on new multi-delayed Chebyshev map. Inf. Process. Lett. 116(11), 674–681 (2016)
Lorenz, E.N.: The Essence of Chaos, 3rd edn. CRC Press, New York (1995)
François, M., Grosges, T., Barchiesi, D., Erra, R.: Pseudo-random number generator based on mixing of three chaotic maps. Commun. Nonlinear Sci. Numer. Simul. 19(4), 887–895 (2014)
Mukhopadhyay, S., Mandal, J.K.: Denoising of digital images through PSO based pixel classification. Cent. Eur. J. Comput. Sci. 3(4), 158–172 (2013)
Mukhopadhyay, S., Mandal, J.K.: A fuzzy switching median filter of impulses in digital imagery (FSMF). Circ. Syst. Sig. Process. 33(7), 2193–2216 (2014). https://doi.org/10.1007/s00034-014-9739-z
Pareek, N.K., Patidar, V., Sud, K.K.: A random bit generator using chaotic maps. Int. J. Netw. Secur. 10(1), 32–38 (2010)
Razali, N.M., Geraghty, J.: Genetic algorithm performance with different selection strategies in solving TSP. In: Proceedings of the World Congress on Engineering, IAENG II, pp. 1134–1139, July 2011
Sanjib Ganguly, D.S.: Distributed generation allocation on radial distribution networks under uncertainties of load and generation using genetic algorithm. IEEE Trans. Sustain. Energ. 6(3), 688–697 (2015)
Sattar, B., Sadkhan, R.S.M.: Proposed random unified chaotic map as PRBG for voice encryption in wireless communication. In: International Conference on Communication, Management and Information Technology, vol. 65, no. 6, pp. 314–323, September 2015
Mukhopadhyay, S., Chaudhuri, T.D., Mandal, J.K.: A hybrid PSO-fuzzy based algorithm for clustering Indian stock market data. In: Mandal, J.K., Dutta, P., Mukhopadhyay, S. (eds.) CICBA 2017. CCIS, vol. 776, pp. 475–487. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6430-2_37
Stoyanov, B., Kordov, K.: Novel secure pseudo-random number generation scheme based on two tinkerbell maps. Adv. Stud. Theor. Phys. 9(9), 411–421 (2015)
Patidar, V., Sud, K.K., Pareek, N.K.: A pseudo random bit generator based on chaotic logistic map and it’s statistical testing. Informatica 33(4), 441–452 (2009)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pal, R., Mukhopadhyay, S. (2019). A Hybrid Model for Optimal Pseudorandom Bit Sequence Generation. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-13-8578-0_14
Download citation
DOI: https://doi.org/10.1007/978-981-13-8578-0_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8577-3
Online ISBN: 978-981-13-8578-0
eBook Packages: Computer ScienceComputer Science (R0)