Modelling Cost-Effectiveness of Defenses in Industrial Control Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9922)

Abstract

Industrial Control Systems (ICS) play a critical role in controlling industrial processes. Wide use of modern IT technologies enables cyber attacks to disrupt the operation of ICS. Advanced Persistent Threats (APT) are the most threatening attacks to ICS due to their long persistence and destructive cyber-physical effects to ICS. This paper considers a simulation of attackers and defenders of an ICS, where the defender must consider the cost-effectiveness of implementing defensive measures within the system in order to create an optimal defense. The aim is to identify the appropriate deployment of a specific defensive strategy, such as defense-in-depth or critical component defense. The problem is represented as a strategic competitive optimisation problem, which is solved using a co-evolutionary particle swarm optimisation algorithm. Through the development of optimal defense strategy, it is possible to identify when each specific defensive strategies is most appropriate; where the optimal defensive strategy depends on the resources available and the relative effectiveness of those resources.

References

  1. 1.
    BSI: Industrial control system security top 10 threats and countermeasures 2014, March 2014. www.allianz-fuer-cybersicherheit.de/ACS/DE/_downloads/techniker/hardware/BSI-CS_005E.pdf
  2. 2.
    Chopitea, T.: Threat modelling of hacktivist groups organization, chain of command, and attack methods (2012). http://publications.lib.chalmers.se/records/fulltext/173222/173222.pdf
  3. 3.
    U.S. Department of Homeland Security: Common cybersecurity vulnerabilities in industrial control systems (2011). www.ics-cert.us-cert.gov/sites/default/files/documents/DHS_Common_Cybersecurity_Vulnerabilities_ICS_20110523.pdf
  4. 4.
    Durkota, K., Lisy, V., Kiekintveld, C., Bosansky, B.: Game-theoretic algorithms for optimal network security hardening using attack graphs. In: Proceedings of International Conference on Autonomous Agents and Multiagent Systems, pp. 1773–1774 (2015)Google Scholar
  5. 5.
    Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of 6th International Symposium on Micro Machine and Human Science, New York, vol. 1, pp. 39–43 (1995)Google Scholar
  6. 6.
    Falliere, N., Murchu, L.O., Chien, E.: W32. Stuxnet dossier. White paper, Symantec Corp., Security. Response 5 (2011)Google Scholar
  7. 7.
    Fielder, A., Panaousis, E., Malacaria, P., Hankin, C., Smeraldi, F.: Game theory meets information security management. In: Cuppens-Boulahia, N., Cuppens, F., Jajodia, S., Abou El Kalam, A., Sans, T. (eds.) SEC 2014. IFIP AICT, vol. 428, pp. 15–29. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  8. 8.
    Fielder, A., Panaousis, E., Malacaria, P., Hankin, C., Smeraldi, F.: Decision support approaches for cyber security investment. Decis. Support Syst. 86, 13–23 (2016)CrossRefGoogle Scholar
  9. 9.
    Gao, K., Jianming, L., Xu, R., Wang, Y., Li, Y.: A hybrid security situation prediction model for information network based on support vector machine and particle swarm optimization. Power Syst. Technol. 4, 033 (2011)Google Scholar
  10. 10.
    Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Schneider, M.: Potassco: the Potsdam answer set solving collection. AI Commun. 24(2), 107–124 (2011)MathSciNetMATHGoogle Scholar
  11. 11.
    Karnan, M., Akila, M.: Personal authentication based on keystroke dynamics using soft computing techniques. In: 2nd International Conference on Communication Software and Networks, ICCSN 2010, pp. 334–338. IEEE (2010)Google Scholar
  12. 12.
    Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Berlin (2010)Google Scholar
  13. 13.
    Klíma, R., Lisý, V., Kiekintveld, C.: Combining online learning and equilibrium computation in security games. In: Khouzani, M.H.R., Panaousis, E., Theodorakopoulos, G. (eds.) GameSec 2015. LNCS, vol. 9406, pp. 130–149. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25594-1_8 CrossRefGoogle Scholar
  14. 14.
    Korzhyk, D., Conitzer, V., Parr, R.: Complexity of computing optimal Stackelberg strategies in security resource allocation games. In: AAAI (2010)Google Scholar
  15. 15.
    Kuipers, D., Fabro, M.: Control Systems Cyber Security: Defense in Depth Strategies. Department of Energy, United States (2006)Google Scholar
  16. 16.
    Lemay, A.: Defending the SCADA network controlling the electrical grid from advanced persistent threats. Ph.D. thesis, École Polytechnique de Montréal (2013)Google Scholar
  17. 17.
    Lippmann, R.P., Ingols, K.W., Scott, C., Piwowarski, K., Kratkiewicz, K.J., Artz, M., Cunningham, R.: Evaluating and Strengthening Enterprise Network Security Using Attack Graphs. Defense Technical Information Center, Fort Belvoir (2005)Google Scholar
  18. 18.
    Ma, Z., Smith, P.: Determining Risks from advanced multi-step attacks to critical information infrastructures. In: Luiijf, E., Hartel, P. (eds.) CRITIS 2013. LNCS, vol. 8328, pp. 142–154. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  19. 19.
    Noel, S., Jajodia, S., Wang, L., Singhal, A.: Measuring security risk of networks using attack graphs. Int. J. Next-Gener. Comput. 1(1), 135–147 (2010)Google Scholar
  20. 20.
    Ou, X., Boyer, W.F., McQueen, M.A.: A scalable approach to attack graph generation. In: Proceedings of 13th ACM Conference on Computer and Communications Security, pp. 336–345. ACM (2006)Google Scholar
  21. 21.
    Pham, V., Cid, C.: Are we compromised? Modelling security assessment games. In: Grossklags, J., Walrand, J. (eds.) GameSec 2012. LNCS, vol. 7638, pp. 234–247. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Poli, R.: Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. Appl. 2008, 3 (2008)Google Scholar
  23. 23.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)CrossRefGoogle Scholar
  24. 24.
    Small, P.E.: Defense in Depth: An Impractical Strategy for a Cyber World. SANS Institute, Bethesda (2011)Google Scholar
  25. 25.
    Srinoy, S.: Intrusion detection model based on particle swarm optimization and support vector machine. In: IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA, pp. 186–192. IEEE (2007)Google Scholar
  26. 26.
    Stouffer, K., Falco, J., Scarfone, K.: Guide to industrial control systems (ICS) security. NIST Special Publication (2011). http://csrc.nist.gov/publications/nistpubs/800-82/SP800-82-final.pdf
  27. 27.
    Tsai, J., Rathi, S., Kiekintveld, C., Ordez, F., Tambe, M.: IRIS - A tool for strategic security allocation in transportation networks, vol. 2, pp. 1327–1334. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 1 (2009)Google Scholar
  28. 28.
    Wang, L., Noel, S., Jajodia, S.: Minimum-cost network hardening using attack graphs. Comput. Commun. 29(18), 3812–3824 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute for Security Science and TechnologyImperial College LondonLondonUK

Personalised recommendations