Manipulating Adversary’s Belief: A Dynamic Game Approach to Deception by Design for Proactive Network Security

  • Karel HorákEmail author
  • Quanyan Zhu
  • Branislav Bošanský
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10575)


Due to the sophisticated nature of current computer systems, traditional defense measures, such as firewalls, malware scanners, and intrusion detection/prevention systems, have been found inadequate. These technological systems suffer from the fact that a sophisticated attacker can study them, identify their weaknesses and thus get an advantage over the defender. To prevent this from happening a proactive cyber defense is a new defense mechanism in which we strategically engage the attacker by using cyber deception techniques, and we influence his actions by creating and reinforcing his view of the computer system. We apply the cyber deception techniques in the field of network security and study the impact of the deception on attacker’s beliefs using the quantitative framework of the game theory. We account for the sequential nature of an attack and investigate how attacker’s belief evolves and influences his actions. We show how the defender should manipulate this belief to prevent the attacker from achieving his goals and thus minimize the damage inflicted to the network. To design a successful defense based on cyber deception, it is crucial to employ strategic thinking and account explicitly for attacker’s belief that he is being exposed to deceptive attempts. By doing so, we can make the deception more believable from the perspective of the attacker.



This research was supported by the Czech Science Foundation (grant no. 15-23235S), NSF grants CNS-1544782 and CNS-1720230, the DOE grant DE-NE0008571, by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-13-2-0045 (ARL Cyber Security CRA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes not with standing any copyright notation here on.


  1. 1.
    Achleitner, S., La Porta, T., McDaniel, P., Sugrim, S., Krishnamurthy, S.V., Chadha, R.: Cyber deception: virtual networks to defend insider reconnaissance. In: Proceedings of the 2016 International Workshop on Managing Insider Security Threats, pp. 57–68. ACM (2016)Google Scholar
  2. 2.
    Başar, T., Olsder, G.J.: Dynamic Noncooperative Game Theory. SIAM, Philadelphia (1998)zbMATHGoogle Scholar
  3. 3.
    Bercovitch, M., Renford, M., Hasson, L., Shabtai, A., Rokach, L., Elovici, Y.: HoneyGen: an automated honeytokens generator. In: IEEE International Conference on Intelligence and Security Informatics, ISI 2011, pp. 131–136. IEEE (2011)Google Scholar
  4. 4.
    Bowen, B.M., Hershkop, S., Keromytis, A.D., Stolfo, S.J.: Baiting inside attackers using decoy documents. In: Chen, Y., Dimitriou, T.D., Zhou, J. (eds.) SecureComm 2009. LNICSSITE, vol. 19, pp. 51–70. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-05284-2_4 CrossRefGoogle Scholar
  5. 5.
    Dagon, D., Qin, X., Gu, G., Lee, W., Grizzard, J., Levine, J., Owen, H.: HoneyStat: local worm detection using honeypots. In: Jonsson, E., Valdes, A., Almgren, M. (eds.) RAID 2004. LNCS, vol. 3224, pp. 39–58. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-30143-1_3 CrossRefGoogle Scholar
  6. 6.
    Durkota, K., Lisý, V., Bošanský, B., Kiekintveld, C.: Approximate solutions for attack graph games with imperfect information. In: Khouzani, M.H.R., Panaousis, E., Theodorakopoulos, G. (eds.) GameSec 2015. LNCS, vol. 9406, pp. 228–249. Springer, Cham (2015). doi: 10.1007/978-3-319-25594-1_13 CrossRefGoogle Scholar
  7. 7.
    Falliere, N., Murchu, L.O., Chien, E.: W32. stuxnet dossier. White Paper Symantec Corp. Secur. Response 5(6), 2–3 (2011).
  8. 8.
    Gostev, A., Soumenkov, I.: Stuxnet/Duqu: The evolution of drivers (2011).
  9. 9.
    Gu, G., Zhang, J., Lee, W.: BotSniffer: detecting botnet command and control channels in network traffic. In: Proceedings of the 15th Annual Network and Distributed System Security Symposium (NDSS 2008) (2008)Google Scholar
  10. 10.
    Hansen, E.A., Bernstein, D.S., Zilberstein, S.: Dynamic programming for partially observable stochastic games. AAAI 4, 709–715 (2004)Google Scholar
  11. 11.
    Horák, K., Bošanský, B., Pěchouček, M.: Heuristic search value iteration for one-sided partially observable stochastic games. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017) (2017)Google Scholar
  12. 12.
    Jajodia, S., Ghosh, A.K., Subrahmanian, V., Swarup, V., Wang, C., Wang, X.S. (eds.): Moving Target Defense II - Application of Game Theory and Adversarial Modeling. Advances in Information Security, vol. 100. Springer, New York (2013)Google Scholar
  13. 13.
    Jajodia, S., Ghosh, A.K., Swarup, V., Wang, C., Wang, X.S. (eds.): Moving Target Defense - Creating Asymmetric Uncertainty for Cyber Threats. Advances in Information Security, vol. 54. Springer, New York (2011)Google Scholar
  14. 14.
    Kreibich, C., Crowcroft, J.: Honeycomb: creating intrusion detection signatures using honeypots. ACM SIGCOMM Comput. Commun. Rev. 34(1), 51–56 (2004)CrossRefGoogle Scholar
  15. 15.
    Kuipers, D., Fabro, M.: Control systems cyber security: Defense in depth strategies. United States, Department of Energy (2006)Google Scholar
  16. 16.
    Manshaei, M.H., Zhu, Q., Alpcan, T., Bacşar, T., Hubaux, J.P.: Game theory meets network security and privacy. ACM Comput. Surv. (CSUR) 45(3), 25 (2013)CrossRefzbMATHGoogle Scholar
  17. 17.
    McRae, C.M., Vaughn, R.B.: Phighting the phisher: using web bugs and honeytokens to investigate the source of phishing attacks. In: 40th Annual Hawaii International Conference on System Sciences 2007, HICSS 2007, p. 270c. IEEE (2007)Google Scholar
  18. 18.
    Mohammadi, A., Manshaei, M.H., Moghaddam, M.M., Zhu, Q.: A game-theoretic analysis of deception over social networks using fake avatars. In: Zhu, Q., Alpcan, T., Panaousis, E., Tambe, M., Casey, W. (eds.) GameSec 2016. LNCS, vol. 9996, pp. 382–394. Springer, Cham (2016). doi: 10.1007/978-3-319-47413-7_22 Google Scholar
  19. 19.
    Osborne, M.J., Rubinstein, A.: A Course in Game Theory. MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  20. 20.
    Pawlick, J., Farhang, S., Zhu, Q.: Flip the cloud: cyber-physical signaling games in the presence of advanced persistent threats. In: Khouzani, M.H.R., Panaousis, E., Theodorakopoulos, G. (eds.) GameSec 2015. LNCS, vol. 9406, pp. 289–308. Springer, Cham (2015). doi: 10.1007/978-3-319-25594-1_16 CrossRefGoogle Scholar
  21. 21.
    Rostami, M., Koushanfar, F., Rajendran, J., Karri, R.: Hardware security: threat models and metrics. In: Proceedings of the International Conference on Computer-Aided Design, pp. 819–823. IEEE Press (2013)Google Scholar
  22. 22.
    Spitzner, L.: Honeypots: Tracking Hackers, vol. 1. Addison-Wesley Reading, Boston (2003)Google Scholar
  23. 23.
    Stech, F.J., Heckman, K.E., Strom, B.E.: Integrating cyber-D&D into adversary modeling for active cyber defense. In: Jajodia, S., Subrahmanian, V., Swarup, V., Wang, C. (eds.) Cyber Deception, pp. 1–22. Springer, Cham (2016). doi: 10.1007/978-3-319-32699-3_1 Google Scholar
  24. 24.
  25. 25.
    Tankard, C.: Advanced persistent threats and how to monitor and deter them. Netw. Secur. 2011(8), 16–19 (2011)CrossRefGoogle Scholar
  26. 26.
    Underbrink, A.: Effective cyber deception. In: Jajodia, S., Subrahmanian, V., Swarup, V., Wang, C. (eds.) Cyber Deception, pp. 115–147. Springer, Cham (2016). doi: 10.1007/978-3-319-32699-3_6 CrossRefGoogle Scholar
  27. 27.
    Vollmer, T., Manic, M.: Cyber-physical system security with deceptive virtual hosts for industrial control networks. IEEE Trans. Industr. Inf. 10(2), 1337–1347 (2014)CrossRefGoogle Scholar
  28. 28.
    Weinstein, W., Lepanto, J.: Camouflage of network traffic to resist attack (CONTRA). In: DARPA Information Survivability Conference and Exposition 2003. Proceedings, vol. 2, pp. 126–127. IEEE (2003)Google Scholar
  29. 29.
    Zhu, Q., Başar, T.: Game-theoretic approach to feedback-driven multi-stage moving target defense. In: Das, S.K., Nita-Rotaru, C., Kantarcioglu, M. (eds.) GameSec 2013. LNCS, vol. 8252, pp. 246–263. Springer, Cham (2013). doi: 10.1007/978-3-319-02786-9_15 CrossRefGoogle Scholar
  30. 30.
    Zhu, Q., Basar, T.: Game-theoretic methods for robustness, security, and resilience of cyberphysical control systems: games-in-games principle for optimal cross-layer resilient control systems. IEEE Control Syst. 35(1), 46–65 (2015)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Zhu, Q., Clark, A., Poovendran, R., Başar, T.: Deceptive routing games. In: IEEE 52nd Annual Conference on Decision and Control (CDC), pp. 2704–2711. IEEE (2012)Google Scholar
  32. 32.
    Zhu, Q., Clark, A., Poovendran, R., Basar, T.: Deployment and exploitation of deceptive honeybots in social networks. In: IEEE 52nd Annual Conference on Decision and Control (CDC), pp. 212–219. IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Karel Horák
    • 1
    Email author
  • Quanyan Zhu
    • 2
  • Branislav Bošanský
    • 1
  1. 1.Department of Computer Science, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic
  2. 2.Department of Electrical and Computer EngineeringNew York UniversityNew YorkUSA

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