Grammatical Evolution with Coevolutionary Algorithms in Cyber Security

  • Erik Hemberg
  • Anthony Erb Lugo
  • Dennis Garcia
  • Una-May O’Reilly


We apply Grammatical Evolution (GE), and multi population competitive coevolutionary algorithms to the domain of cybersecurity. Our interest (and concern) is the evolution of network denial of service attacks. In these cases, when attackers are deterred by a specific defense, they evolve their strategies until variations find success. Defenders are then forced to counter the new variations and an arms race ensues. We use GE and grammars to conveniently express and explore the behavior of network defenses and denial of service attacks under different mission and network scenarios. We use coevolution to model competition between attacks and defenses and the larger scale arms race. This allows us to study the dynamics and the solutions of the competing adversaries.



This material is based upon the work supported by DARPA. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements. Either expressed or implied of Applied Communication Services, or the US Government.


  1. 1.
    K. Adamu, S. Phelps, Coevolutionary grammatical evolution for building trading algorithms, in Electrical Engineering and Applied Computing (Springer, Berlin, 2011), pp. 311–322Google Scholar
  2. 2.
    M. Alfonseca, S. Gil, Evolving a predator–prey ecosystem of mathematical expressions with grammatical evolution. Complexity 20(3), 66–83 (2015)CrossRefGoogle Scholar
  3. 3.
    R.M.A. Azad, C. Ryan, An examination of simultaneous evolution of grammars and solutions, in Genetic Programming Theory and Practice III (Springer, Berlin, 2006), pp. 141–158Google Scholar
  4. 4.
    J.C. Bongard, H. Lipson, Nonlinear system identification using coevolution of models and tests. IEEE Trans. Evol. Comput. 9(4), 361–384 (2005)CrossRefGoogle Scholar
  5. 5.
    E.D. de Jong, A monotonic archive for pareto-coevolution. Evol. Comput. 15(1), 61–93 (2007)CrossRefGoogle Scholar
  6. 6.
    E. De Jong, The maxsolve algorithm for coevolution, in Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation (ACM, New York, 2005), pp. 483–489Google Scholar
  7. 7.
    I. Dempsey, M. O’Neill, A. Brabazon, Foundations in Grammatical Evolution for Dynamic Environments, vol. 194 (Springer, Berlin, 2009)Google Scholar
  8. 8.
    S.G. Ficici, Solution concepts in coevolutionary algorithms. PhD thesis, Brandeis University, 2004Google Scholar
  9. 9.
    P. Gabrielsson, U. Johansson, R. Konig, Co-evolving online high-frequency trading strategies using grammatical evolution, in 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr) (IEEE, New York, 2014), pp. 473–480CrossRefGoogle Scholar
  10. 10.
    D. Garcia, A.E. Lugo, E. Hemberg, U.-M. O’Reilly, Investigating coevolutionary archive based genetic algorithms on cyber defense networks, in Proceedings of the Genetic and Evolutionary Computation Conference Companion (ACM, New York, 2017), pp. 1455–1462Google Scholar
  11. 11.
    R. Harper, Evolving robocode tanks for Evo robocode. Genet. Program Evolvable Mach. 15(4), 403–431 (2014)CrossRefGoogle Scholar
  12. 12.
    E. Hemberg, J. Rosen, G. Warner, S. Wijesinghe, U.-M. O’Reilly, Detecting tax evasion: a co-evolutionary approach. Artif. Intell. Law 24(2), 149–182 (2016)CrossRefGoogle Scholar
  13. 13.
    M.I. Heywood, Evolutionary model building under streaming data for classification tasks: opportunities and challenges. Genet. Program Evolvable Mach. 16(3), 283–326 (2015)MathSciNetCrossRefGoogle Scholar
  14. 14.
    K. Krawiec, M. Heywood, Solving complex problems with coevolutionary algorithms, in Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion (ACM, New York, 2016), pp. 687–713Google Scholar
  15. 15.
    A. Kuzmanovic, E.W. Knightly, Low-rate TCP-targeted denial of service attacks: the shrew vs. the mice and elephants, in Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (ACM, New York, 2003), pp. 75–86Google Scholar
  16. 16.
    C. Le Goues, A. Nguyen-Tuong, H. Chen, J.W. Davidson, S. Forrest, J.D. Hiser, J.C. Knight, M. Van Gundy, Moving target defenses in the helix self-regenerative architecture, in Moving Target Defense II (Springer, Berlin, 2013), pp. 117–149CrossRefGoogle Scholar
  17. 17.
    A.R. McIntyre, M.I. Heywood, Multi-objective competitive coevolution for efficient GP classifier problem decomposition, in IEEE International Conference on Systems, Man and Cybernetics, 2007. ISIC (IEEE, New York, 2007), pp. 1930–1937Google Scholar
  18. 18.
    A.R. McIntyre, M.I. Heywood, Cooperative problem decomposition in pareto competitive classifier models of coevolution, in Genetic Programming (Springer, Berlin, 2008), pp. 289–300Google Scholar
  19. 19.
    E. Popovici, A. Bucci, R.P. Wiegand, E.D. De Jong, Coevolutionary principles, in Handbook of Natural Computing (Springer, Berlin, 2012), pp. 987–1033CrossRefGoogle Scholar
  20. 20.
    G. Rush, D.R. Tauritz, A.D. Kent, Coevolutionary agent-based network defense lightweight event system (candles), in Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference (ACM, New York, 2015), pp. 859–866Google Scholar
  21. 21.
    K.O. Stanley, R. Miikkulainen, Competitive coevolution through evolutionary complexification. J. Artif. Intell. Res. (JAIR) 21, 63–100 (2004)CrossRefGoogle Scholar
  22. 22.
    I. Stoica, R. Morris, D. Karger, M.F. Kaashoek, H. Balakrishnan, Chord: a scalable peer-to-peer lookup service for internet applications. ACM SIGCOMM Comput. Commun. Rev. 31(4), 149–160 (2001)CrossRefGoogle Scholar
  23. 23.
    D.R. Tauritz, A no-free-lunch framework for coevolution, in Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (ACM, New York, 2008), pp. 371–378Google Scholar
  24. 24.
    P.A. Whigham, G. Dick, J. Maclaurin, C.A. Owen, Examining the best of both worlds of grammatical evolution, in Proceedings of the 2015 on Genetic and Evolutionary Computation Conference (ACM, New York, 2015), pp. 1111–1118Google Scholar
  25. 25.
    M.L. Winterrose, K.M. Carter, Strategic evolution of adversaries against temporal platform diversity active cyber defenses, in Proceedings of the 2014 Symposium on Agent Directed Simulation (Society for Computer Simulation International, 2014), p. 9Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Erik Hemberg
    • 1
  • Anthony Erb Lugo
    • 1
  • Dennis Garcia
    • 1
  • Una-May O’Reilly
    • 1
  1. 1.Computer Science and Artificial Intelligence Lab (CSAIL)MITBostonUSA

Personalised recommendations