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Exploring Adversarial Artificial Intelligence for Autonomous Adaptive Cyber Defense

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Adaptive Autonomous Secure Cyber Systems

Abstract

Cyber adversaries are immersed in a ceaseless arms race. Each adversary incessantly maneuvers to adapt to the opposing posture. An avenue to pro-active, adversarially-hardened cyber defenses can be investigated by studying the dynamics of these cyber engagements. An adversarial engagement can computationally act as an elementary component of a competitive coevolutionary system which generates many autonomous arms races that can be harvested for robust defensive solutions. We present a framework that recreates the coevolutionary process in the context of network cyber security scenarios. We describe its current use cases and an exploration in how to harvest defensive solutions from it using different solution concepts and solution quality measures.

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References

  1. Stefan Achleitner, Thomas Laporta, and Patrick McDaniel. Cyber deception: Virtual networks to defend insider reconnaissance. In Proceedings of the 2016 International Workshop on Managing Insider Security Threats, pages 57–68, 2016.

    Google Scholar 

  2. Peter J. Angeline and Jordan B. Pollack. Competitive environments evolve better solutions for complex tasks. In Proceedings of the Fifth International Conference (GA93), Genetic Algorithms, pages 264–270, 1993.

    Google Scholar 

  3. Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang. Generalization and Equilibrium in Generative Adversarial Nets (GANs). arXiv preprint arXiv:1703.00573, 2017.

    Google Scholar 

  4. Kai Arulkumaran, Antoine Cully, and Julian Togelius. Alphastar: An evolutionary computation perspective. arXiv preprint arXiv:1902.01724, 2019.

    Google Scholar 

  5. Thomas Bäck. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, 1996.

    MATH  Google Scholar 

  6. David Balduzzi, Karl Tuyls, Julien Perolat, and Thore Graepel. Re-evaluating evaluation. In Advances in Neural Information Processing Systems, pages 3272–3283, 2018.

    Google Scholar 

  7. Josh C Bongard and Hod Lipson. Nonlinear system identification using coevolution of models and tests. IEEE Transactions on Evolutionary Computation, 9(4):361–384, 2005.

    Article  Google Scholar 

  8. A. B. Cardona, J. Togelius, and M. J. Nelson. Competitive coevolution in ms. pac-man. In 2013 IEEE Congress on Evolutionary Computation, pages 1403–1410, June 2013.

    Google Scholar 

  9. Edwin De Jong. The maxsolve algorithm for coevolution. In Proceedings of the 7th annual conference on Genetic and evolutionary computation, pages 483–489. ACM, 2005.

    Google Scholar 

  10. Edwin D. De Jong. A monotonic archive for pareto-coevolution. Evol. Comput., 15(1):61–93, March 2007.

    Article  Google Scholar 

  11. Sevan Gregory Ficici. Solution concepts in coevolutionary algorithms. PhD thesis, Citeseer, 2004.

    Google Scholar 

  12. D Fogel. Blondie24: Playing at the edge of artificial intelligence, 2001.

    Google Scholar 

  13. Dennis Garcia, Anthony Erb Lugo, Erik Hemberg, and Una-May O’Reilly. Investigating coevolutionary archive based genetic algorithms on cyber defense networks. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’17, pages 1455–1462, New York, NY, USA, 2017. ACM.

    Google Scholar 

  14. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems, pages 2672–2680, 2014.

    Google Scholar 

  15. Erik Hemberg, Joseph R Zipkin, Richard W Skowyra, Neal Wagner, and Una-May O’Reilly. Adversarial co-evolution of attack and defense in a segmented computer network environment. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1648–1655. ACM, 2018.

    Google Scholar 

  16. Jonathan Kelly, Michael DeLaus, Erik Hemberg, and Una-May O’Reilly. Adversarially adapting deceptive views and reconnaissance scans on a software defined network. In 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pages 49–54. IEEE, 2019.

    Google Scholar 

  17. Keith Kirkpatrick. Software-defined networking. Communications of the ACM, 56(9), 2013.

    Google Scholar 

  18. Krzysztof Krawiec and Malcolm Heywood. Solving complex problems with coevolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 880–906. ACM, 2018.

    Google Scholar 

  19. Mona Lange, Alexander Kott, Noam Ben-Asher, Wim Mees, Nazife Baykal, Cristian-Mihai Vidu, Matteo Merialdo, Marek Malowidzki, and Bhopinder Madahar. Recommendations for model-driven paradigms for integrated approaches to cyber defense. arXiv preprint arXiv:1703.03306, 2017.

    Google Scholar 

  20. Chong-U Lim, Robin Baumgarten, and Simon Colton. Evolving behaviour trees for the commercial game DEFCON. In European Conference on the Applications of Evolutionary Computation, pages 100–110. Springer, 2010.

    Google Scholar 

  21. Pawel Liskowski and Krzysztof Krawiec. Discovery of implicit objectives by compression of interaction matrix in test-based problems. In International Conference on Parallel Problem Solving from Nature, pages 611–620. Springer, 2014.

    Google Scholar 

  22. Paweł Liskowski and Krzysztof Krawiec. Non-negative matrix factorization for unsupervised derivation of search objectives in genetic programming. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pages 749–756. ACM, 2016.

    Google Scholar 

  23. Gordon Lyon. Nmap network scanner. https://nmap.org/, 2018. [Online; accessed 6-July-2018].

  24. McLennan Andrew M. McKelvey, Richard D. and Theodore L. Turocy. Gambit: Software tools for game theory, 2016.

    Google Scholar 

  25. Thomas Miconi. Why coevolution doesn’t “work”: superiority and progress in coevolution. In European Conference on Genetic Programming, pages 49–60. Springer Berlin Heidelberg, 2009.

    Google Scholar 

  26. Barton P Miller, Louis Fredriksen, and Bryan So. An empirical study of the reliability of unix utilities. Communications of the ACM, 33(12):32–44, 1990.

    Article  Google Scholar 

  27. Melanie Mitchell. Coevolutionary learning with spatially distributed populations. Computational intelligence: principles and practice, 2006.

    Google Scholar 

  28. Roger B Myerson. Game theory. Harvard university press, 2013.

    Google Scholar 

  29. Michael O’Neill and Conor Ryan. Grammatical evolution: evolutionary automatic programming in an arbitrary language, volume 4. Springer, 2003.

    Book  Google Scholar 

  30. Martin J. Osborne and Ariel Rubinstein. A course in game theory. The MIT Press, Cambridge, USA, 1994. electronic edition.

    Google Scholar 

  31. Una-May O’Reilly and Erik Hemberg. An artificial coevolutionary framework for adversarial ai. In AAAI Fall Symposia, 2018.

    Google Scholar 

  32. Marcos Pertierra. Investigating coevolutionary algorithms for expensive fitness evaluations in cybersecurity. Master’s thesis, Massachusetts Institute of Technology, 2018.

    Google Scholar 

  33. Marcos Pertierra Arrojo. Investigating coevolutionary algorithms for expensive fitness evaluations in cybersecurity, 2018.

    Google Scholar 

  34. Elena Popovici, Anthony Bucci, R Paul Wiegand, and Edwin D De Jong. Coevolutionary principles. In Handbook of natural computing, pages 987–1033. Springer, 2012.

    Google Scholar 

  35. Daniel Prado Sanchez. Visualizing adversaries - transparent pooling approaches for decision support in cybersecurity. Master’s thesis, Massachusetts Institute of Technology, 2018.

    Google Scholar 

  36. Christopher D Rosin and Richard K Belew. New methods for competitive coevolution. Evolutionary Computation, 5(1):1–29, 1997.

    Article  Google Scholar 

  37. Franz Rothlauf. Design of modern heuristics: principles and application. Springer Science & Business Media, 2011.

    Book  Google Scholar 

  38. George Rush, Daniel R Tauritz, and Alexander 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, pages 859–866. ACM, 2015.

    Google Scholar 

  39. Spyridon Samothrakis, Simon Lucas, ThomasPhilip Runarsson, and David Robles. Coevolving game-playing agents: Measuring performance and intransitivities. IEEE Transactions on Evolutionary Computation, 17(2):213–226, 2013.

    Article  Google Scholar 

  40. Daniel Prado Sanchez, Marcos A Pertierra, Erik Hemberg, and Una-May O’Reilly. Competitive coevolutionary algorithm decision support. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 300–301. ACM, 2018.

    Google Scholar 

  41. Karl Sims. Evolving 3d morphology and behavior by competition. Artificial life, 1(4):353–372, 1994.

    Article  Google Scholar 

  42. You Seok Son and Ross Baldick. Hybrid coevolutionary programming for nash equilibrium search in games with local optima. IEEE Transactions on Evolutionary Computation, 8(4):305–315, 2004.

    Article  Google Scholar 

  43. Aditya Sood and Richard Enbody. Targeted cyberattacks: a superset of advanced persistent threats. IEEE security & privacy, 11(1):54–61, 2013.

    Google Scholar 

  44. Milind Tambe, editor. Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned. Cambridge University Press, 2012.

    Google Scholar 

  45. Peter D. Taylor and Leo B. Jonker. Evolutionary stable strategies and game dynamics. Mathematical Biosciences, 40(1):145–156, 1978.

    Article  MathSciNet  Google Scholar 

  46. Mininet Team. Mininet - realistic virtual sdn network emulator. http://mininet.org/, 2018. [Online; accessed 6-July-2018].

  47. Brian Thompson, James Morris-King, and Hasan Cam. Controlling risk of data exfiltration in cyber networks due to stealthy propagating malware. In Military Communications Conference, MILCOM 2016-2016 IEEE, pages 479–484. IEEE, 2016.

    Google Scholar 

  48. Nathan Williams and Melanie Mitchell. Investigating the success of spatial coevolution. In Proceedings of the 7th annual conference on Genetic and evolutionary computation, pages 523–530. ACM, 2005.

    Google Scholar 

  49. Michael L Winterrose and Kevin M Carter. Strategic evolution of adversaries against temporal platform diversity active cyber defenses. In Proceedings of the 2014 Symposium on Agent Directed Simulation, page 9. Society for Computer Simulation International, 2014.

    Google Scholar 

  50. Forhad Zaman, Saber M Elsayed, Tapabrata Ray, and Ruhul A Sarkerr. Evolutionary algorithms for finding nash equilibria in electricity markets. IEEE Transactions on Evolutionary Computation, 22(4):536–549, 2018.

    Article  Google Scholar 

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Acknowledgements

This material is based upon 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.

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Authors and Affiliations

  1. MIT CSAIL, Cambridge, MA, USA

    Erik Hemberg, Linda Zhang & Una-May O’Reilly

Authors
  1. Erik Hemberg
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  2. Linda Zhang
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  3. Una-May O’Reilly
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Corresponding author

Correspondence to Erik Hemberg .

Editor information

Editors and Affiliations

  1. Center for Secure Information Systems, George Mason University, Fairfax, VA, USA

    Sushil Jajodia

  2. Thayer School of Engineering, Dartmouth College, Hanover, NH, USA

    George Cybenko

  3. Department of Computer Science, Dartmouth College, Hanover, NH, USA

    V.S. Subrahmanian

  4. MS T310, MITRE Corporation, McLean, VA, USA

    Vipin Swarup

  5. Computing and Information Science Division, Army Research Office, Durham, NC, USA

    Cliff Wang

  6. Computer Science & Engineering, University of Michigan, Ann Arbor, MI, USA

    Michael Wellman

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Hemberg, E., Zhang, L., O’Reilly, UM. (2020). Exploring Adversarial Artificial Intelligence for Autonomous Adaptive Cyber Defense. In: Jajodia, S., Cybenko, G., Subrahmanian, V., Swarup, V., Wang, C., Wellman, M. (eds) Adaptive Autonomous Secure Cyber Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-33432-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-33432-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33431-4

  • Online ISBN: 978-3-030-33432-1

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