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Lamar: A New Pseudorandom Number Generator Evolved by Means of Genetic Programming

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Parallel Problem Solving from Nature - PPSN IX (PPSN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4193))

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Abstract

Pseudorandom number generation is a key component of many Computer Science algorithms, including mathematical modeling, stochastic processes, Monte Carlo simulations, and most cryptographic primitives and protocols. To date, multiple approaches that use Evolutionary Computation (EC) techniques have been proposed for designing useful Pseudorandom Number Generators (PRNGs) for certain non-cryptographic applications. However, none of the proposals have been secure nor efficient enough to be of interest for the much more demanding crypto world. In this work, we present a general scheme, which uses Genetic Programming (GP), for the automatic design of crypto-quality PRNGs by evolving highly nonlinear and extremely efficient functions. A new PRNG named Lamar and obtained using this scheme is proposed, whose C code and preliminary security analysis are provided.

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References

  1. The lil-gp GP system, http://garage.cps.msu.edu/software/lil-gp/

  2. Bao, F.: Cryptanalysis of a partially known cellular automata cryptosystem. IEEE Trans. on Computers 53(11), 1493–1497 (2004)

    Article  Google Scholar 

  3. Cantú-Paz, E.: On random numbers and the performance of genetic algorithms. In: Proc. of GECCO 2002, vol. 2, pp. 311–318. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  4. Forré, R.: The strict avalanche criterion: Spectral properties of boolean functions and an extended definition. In: Goldwasser, S. (ed.) CRYPTO 1988. LNCS, vol. 403, pp. 450–468. Springer, Heidelberg (1990)

    Chapter  Google Scholar 

  5. Hernandez-Castro, J.C., Isasi, P., Seznec, A.: On the design of state-of-the-art PRNGs by means of genetic programming. In: Proc. of the IEEE CEC 2004, pp. 1510–1516. IEEE Press, Los Alamitos (2004)

    Google Scholar 

  6. Hernandez-Castro, J.C., Ribagorda, A., Isasi, P., Sierra, J.M.: Finding near optimal parameters for linear congruential PRNGs by means of evolutionary computation. In: Proc. of GECCO 2001, pp. 1292–1298. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  7. Hinton, G., et al.: The microarchitecture of the pentium 4 processor. Intel Technology Journal Q1 (2001)

    Google Scholar 

  8. Hirose, S., Yoshida, S.: A one-way hash function based on a two-dimensional cellular automaton. In: Proc. of the 20th Symposium on Information Theory and its Applications, Matsuyama, vol. 1, pp. 213–216 (1997)

    Google Scholar 

  9. Johnson, B.C.: Radix-b extensions to some common empirical tests for PRNGs. ACM Trans. on Modeling and Comp. Sim. 6(4), 261–273 (1996)

    Article  Google Scholar 

  10. Kanter, I., Kinzel, W., Kanter, E.: Secure exchange of information by synchronization of neural networks. Europhysical Letters 57(141) (2002)

    Google Scholar 

  11. Knuth, D.E.: The Art of Computer Programming. Seminumerical Algorithms, 3rd edn., vol. 2. Addison-Wesley, Reading (1998)

    MATH  Google Scholar 

  12. Koza, J.R.: Evolving a computer program to generate random number using the genetic programming paradigm. In: Proc. of the 4th Int. Conference on Genetic Algorithms, pp. 37–44. Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

  13. Marsaglia, G.: Yet another RNG. Posted to sci.stat.math (1994)

    Google Scholar 

  14. Marsaglia, G.: The Marsaglia Random Number CDROM Including the DIEHARD Battery of Tests of Randomness (1996), http://stat.fsu.edu/pub/diehard

  15. Marsaglia, G., Tsang, W.W.: Some difficult-to-pass tests of randomness. Journal of Statistical Software 7(3) (2002)

    Google Scholar 

  16. Matsumoto, M., Kurita, Y.: Twisted GFSR generators. ACM Trans. on Modeling and Comp. Sim. 2(3), 179–194 (1992)

    Article  Google Scholar 

  17. Matsumoto, M., et al.: Mersenne twister: A 623-dimensionally equidistributed uniform PRNG. ACM Trans. on Modeling and Comp. Sim. 8(1), 3–30 (1998)

    Article  Google Scholar 

  18. Meysenburg, M.M., Foster, J.A.: The quality of PRNGs and simple genetic algorithm performance. In: Proc. of the 7th Int. Conference on Genetic Algorithms, pp. 276–281. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  19. Meysenburg, M.M., Foster, J.A.: Randomness and GA performance, revisited. In: Proc. of GECCO 1999, vol. 1, pp. 425–432. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  20. Mihaljevic, M., Zheng, Y., Imai, H.: A cellular automaton based fast one-way hash function suitable for hardware implementation. In: Imai, H., Zheng, Y. (eds.) PKC 1998. LNCS, vol. 1431, Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  21. Mihaljevic, M.J.: An improved key stream generator based on the programmable cellular automata. In: Han, Y., Quing, S. (eds.) ICICS 1997. LNCS, vol. 1334, pp. 181–191. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  22. Millan, W., Clark, A., Dawson, E.: An effective genetic algorithm for finding boolean functions. In: Han, Y., Quing, S. (eds.) ICICS 1997. LNCS, vol. 1334, Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  23. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, 2nd edn. Cambridge University Press, Cambridge (1992)

    MATH  Google Scholar 

  24. Rivest, R.L., Robshaw, M.J.B., Sidney, R., Yin, Y.L.: The RC6 block cipher, v1.1 (August 20, 1998)

    Google Scholar 

  25. Rukhin, A., et al.: A statistical test suite for random and pseudorandom number generators for cryptographic applications. NIST special publication 800-22 (2001), http://csrc.nist.gov/rng/

  26. Schneier, B.: Applied Cryptography. John Wiley and Sons, Chichester (1994)

    MATH  Google Scholar 

  27. Seredynski, M., Bouvry, P.: Block cipher based on reversible cellular automata. Next Generation Computing Journal 23(3), 245–258 (2005)

    Article  Google Scholar 

  28. Sipper, M., Tomassini, M.: Generating parallel random number generators by cellular programming. Int. Journal of Modern Physics C, 181–190 (1996)

    Google Scholar 

  29. Tezuka, S., L’Ecuyer, P.: Efficient and portable combined Tausworthe Random Number Generators. ACM Trans. on Modeling and Comp. Sim. 1(2), 99–112 (1991)

    Article  Google Scholar 

  30. Walker, J.: ENT Randomness Tests (1998), http://www.fourmilab.ch/random/

  31. Wolfram, S.: Random sequence generation by cellular automata. Advances in Applied Mathematics 7, 123–169 (1986)

    Article  MathSciNet  Google Scholar 

  32. Yalcin, M.E., Suykens, J.A.K., Vandewalle, J.: True random bit generation from a double-scroll attractor. IEEE Trans. on Circuits and Systems-I: Regular Papers 51(7), 1395–1404 (2004)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Carlos III University of Madrid, Spain

    Carlos Lamenca-Martinez, Julio Cesar Hernandez-Castro, Juan M. Estevez-Tapiador & Arturo Ribagorda

Authors
  1. Carlos Lamenca-Martinez
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  2. Julio Cesar Hernandez-Castro
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  3. Juan M. Estevez-Tapiador
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  4. Arturo Ribagorda
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Editor information

Editors and Affiliations

  1. Science Institute, University of Iceland, P.O. Box, Iceland

    Thomas Philip Runarsson

  2. Vorarlberg University of Applied Sciences, Hochschulstr. 1, A-6850, Dornbirn, Austria

    Hans-Georg Beyer

  3. Automated Scheduling, Optimisation and Planning Group, School of Computer Science & IT, University of Nottingham, NG8 1BB, Nottingham, UK

    Edmund Burke

  4. Depto. Arquitectura y Tecnologa de Computadores, ETS Ingeiera Informtica, C/Daniel Saucedo Aranda, s/n, 18071, Granada, Spain

    Juan J. Merelo-Guervós

  5. Colorado State University, 80523, Fort Collins, CO, USA

    L. Darrell Whitley

  6. University of Birmingham, Birmingham, UK

    Xin Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Lamenca-Martinez, C., Hernandez-Castro, J.C., Estevez-Tapiador, J.M., Ribagorda, A. (2006). Lamar: A New Pseudorandom Number Generator Evolved by Means of Genetic Programming. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_86

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  • DOI: https://doi.org/10.1007/11844297_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38990-3

  • Online ISBN: 978-3-540-38991-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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