skip to main content
research-article

Decision Networks for Security Risk Assessment of Critical Infrastructures

Published:06 March 2018Publication History
Skip Abstract Section

Abstract

We exploit Decision Networks (DN) for the analysis of attack/defense scenarios in critical infrastructures. DN extend Bayesian Networks (BN) with decision and value nodes. DN inherit from BN the possibility to naturally address uncertainty at every level, making possible the modeling of situations that are not limited to Boolean combinations of events. By means of decision nodes, DN can include the interaction level of attacks and countermeasures. Inference algorithms can be directly exploited for implementing a probabilistic analysis of both the risk and the importance of the attacks. Thanks to value nodes, a sound decision theoretic analysis has the goal of selecting the optimal set of countermeasures to activate.

References

  1. X. An, D. Jutla, and N. Cercone. 2006. Privacy intrusion detection using dynamic Bayesian networks. In Proceedings of the International Conference for Electronic Commerce. 208--215. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. Bistarelli, F. Fioravanti, and P. Peretti. 2006. Defense trees for economic evaluation of security investments. In Proceedings of the International Conference on Availability, Reliability and Security. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. E. Borgonovo. 2007. Differential, criticality, and Birnbaum importance measures: An application to basic event, groups, and SSCs in event trees and binary decision diagrams. Reliabil. Eng. Syst. Safety 92, 10 (2007), 1458--1467.Google ScholarGoogle ScholarCross RefCross Ref
  4. E. Byres, D. Leversage, and N. Kube. 2007. Security incidents and trends in SCADA and process industries. Industr. Ethernet Book 39 (2007), 12--20.Google ScholarGoogle Scholar
  5. J. Byres, M. Franz, and D. Miller. 2004. The use of attack trees in assessing vulnerabilities in SCADA systems. In Proceedings of the International Infrastructure Survivability Workshop. Lisbon.Google ScholarGoogle Scholar
  6. H. Chan and A. Darwiche. 2002. When do numbers really matter? J. Artific. Intell. Res. 17 (2002), 265--287. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. H. Chan and A. Darwiche. 2004. Sensitivity analysis in bayesian networks: From single to multiple parameters. In Proceedings of the Conference on Uncertainty in Artificial Intelligence. AUAI Press, 67--75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Y. Cherdantseva, P. Burnap, A. Blyth, P. Eden, K. Jones, H. Soulsby, and K. Stoddart. 2016. A review of cyber security risk assessment methods for SCADA systems. Comput. Secur. 56 (2016), 1--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. D. Codetta. 2013. Generalized fault trees: From reliability to security. In Proceedings of the International Workshop on Quantitative Aspects in Security Assurance. London, UK.Google ScholarGoogle Scholar
  10. D. Codetta and R. Nai. 2010. Evaluation of communication scenarios inside the electrical power system. Int. J. Model. Simul. 30 (2010), 345--352. Issue 3.Google ScholarGoogle ScholarCross RefCross Ref
  11. D. Codetta, L. Portinale, and R. Terruggia. 2014. Decision networks for modeling and analysis of attack/defense scenarios in critical infrastructures. In Proceedings of the International Florida Artificial Intelligence Research Society Conference. Pensacola Beach, FL, 24--27.Google ScholarGoogle Scholar
  12. D. Codetta, L. Portinale, and R. Terruggia. 2014. Quantitative evaluation of attack/defense scenarios through decision network modelling and analysis. In Proceedings of the International Carnahan Conference on Security Technology. 432--437.Google ScholarGoogle Scholar
  13. R. G. Cowell, A. P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter. 1999. Probabilistic Networks and Expert Systems. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Dacier and Y. Deswarte. 1994. Privilege graph: An extension to the typed access matrix model. In Computer Security. Springer, 319--334. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. G. C. Dalton, R. F. Mills, J. M. Colombi, and R. A. Raines. 2006. Analyzing attack trees using generalized stochastic Petri nets. In Proceedings of the Information Assurance Workshop. IEEE, 116--123.Google ScholarGoogle Scholar
  16. G. Dondossola, F. Garrone, and J. Szanto. 2009. Supporting cyber risk assessment of power control systems with experimental data. In Proceedings of the Power Systems Conference and Exposition. IEEE/PES.Google ScholarGoogle Scholar
  17. M. Ekstedt and T. Sommestadt. 2009. Enterprise architecture models for cyber-security analysis. In Proceedings of the Power System Conference and Exposition. IEEE/PES.Google ScholarGoogle Scholar
  18. M. Frigault, L. Wang, A. Singhal, and S. Jajodia. 2008. Measuring network security using dynamic Bayesian network. In Proceedings of the ACM Workshop on Quality of Protection. 23--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. V. Gupta, V. Lam, H. V. Ramasamy, W. H. Sanders, and S. Singh. 2003. Dependability and performance evaluation of intrusion-tolerant server architectures. In Dependable Computing. Springer, 81--101.Google ScholarGoogle Scholar
  20. G. Helmer, J. Wong, M. Slagell, V. Honavar, L. Miller, Y. Wang, X. Wang, and N. Stakhanova. 2007. Software fault tree and coloured Petri net--based specification, design and implementation of agent-based intrusion detection systems. Int. J. Info. Comput. Secur. 1, 1 (2007), 109--142. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. H. M. Henry, R. M. Layer, K. Z. Snow, and D. R. Zaret. 2009. Evaluating the risk of cyber attacks on SCADA systems via Petri net analysis with application to hazardous liquid loading operations. In Proceedings of the Conference on Technologies for Homeland Security. IEEE, 607--614.Google ScholarGoogle Scholar
  22. F. V. Jensen and T. D. Nielsen. 2007. Bayesian Networks and Decision Graphs (2nd ed.). Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. U. B. Kjaerulff and A. L. Madsen. 2008. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Information Science and Statistics. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. D. Koller and N. Friedman. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. B. Kordy, S. Mauw, S. Radomirović, and P. Schweitzer. 2010. Foundations of attack--Defense trees. In International Workshop on Formal Aspects in Security and Trust. Springer, Berlin, Heidelberg, 80--95. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. B. Kordy, L. Piètre-Cambacédès, and P. Schweitzer. 2014. DAG-based attack and defense modeling: Don’t miss the forest for the attack trees. Comput. Sci. Rev. 13 (2014), 1--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. S. Kriaa, M. Bouissou, and L. Piétre-Cambacédés. 2012. Modeling the stuxnet attack with BDMP: Towards more formal risk assessments. In Proceedings of the International Conference on Risk and Security of Internet and Systems. IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. S. L. Lauritzen and D. Nilsson. 2001. Representing and solving decision problems with limited information. Manage. Sci. 47 (2001), 1235--1251.Google ScholarGoogle Scholar
  29. E. LeMay, M. D. Ford, K. Keefe, W. H. Sanders, and C. Muehrcke. 2011. Model-based security metrics using adversary view security evaluation (advise). In Proceedings of the International Conference on Quantitative Evaluation of Systems. IEEE, 191--200. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. D. D. Maua, C. P. de Campos, and M. Zaffalon. 2012. Solving limited memory influence diagrams. Int. J. Artific. Intell. Res. 44 (2012), 97--140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. J. P. McDermott. 2000. Attack net penetration testing. In Proceedings of the Workshop on New Security Paradigms. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. F. C. Meng. 2000. Relationships of fussell--Vesely and birnbaum importance to structural importance in coherent systems. Reliabil. Eng. Syst. Safety 67 (2000), 55--60.Google ScholarGoogle ScholarCross RefCross Ref
  33. L. Portinale and D. Codetta. 2015. Modeling and Analysis of Dependable Systems: A Probabilistic Graphical Model Perspective. World Scientific Publishing. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. S. Pudar, G. Manimaran, and C. Liu. 2009. PENET: A practical method and tool for integrated modeling of security attacks and countermeasures. Comput. Secur. 28, 8 (2009), 754--771. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. X. Qin and W. Lee. 2004. Attack plan recognition and prediction using causal networks. In Proceedings of the Annual Computer Security Application Conference. 370--379. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. A. Roy, D. S. Kim, and K. Trivedi. 2012. Scalable optimal countermeasure selection using implicit enmeration on attack countermeasure trees. In Proceedings of the International Conference on Dependable Systems and Networks. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. J. J. C. H. Ryan and D. J. Ryan. 2006. Expected benefits of information security investments. Comput. Secur. 25, 8 (2006), 579--588. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. O. Scheyner. 2004. Scenario Graphs and Attack Graphs. Ph.D. Dissertation. Carnegie Mellon University. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. B. Schneier. 2000. Secrets and Lies: Digital Security in a Networked World. J. Wiley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. S. Singh, M. Cukier, and W. H. Sanders. 2003. Probabilistic validation of an intrusion-tolerant replication system. In Proceedings of the International Conference on Dependable Systems and Networks. IEEE Computer Society, 615--624.Google ScholarGoogle Scholar
  41. T. Sommestad, M. Ekstedt, and P. Johnson. 2009. Cyber security risks assessment with bayesian defense graphs and architectural models. In Proceedings of the Hawaii International Conference on System Sciences. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. P. Chee-Wooi Ten, Chen-Ching Liu, and M. Govindarasu. 2007. Vulnerability assessment of cybersecurity for SCADA systems using attack trees. In Proceedings of the IEEE Power Engineering Society General Meeting.Google ScholarGoogle Scholar
  43. P. Chee-Wooi Ten, G. Manimaran, and C. C. Liu. 2010. Cybersecurity for critical infrastructures: Attack and defense modeling. IEEE Trans. Syst. Man Cybernet., Part A 40 (2010), 853--65. Issue 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. V. Verendel. 2009. Quantified security is a weak hypothesis: A critical survey of results and assumptions. In Proceedings of the New Security Paradigms Workshop. ACM, 37--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. P. Xie, J. H. Li, X. Ou, P. Liu, and R. Levy. 2010. Using bayesian networks for cyber-security analysis. In Proceedings of the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN’10). 211--220.Google ScholarGoogle Scholar
  46. S. Zhang and S. Song. 2011. A novel attack graph posterior inference model based on Bayesian network. J. Info. Secur. 2, 1 (2011), 8--27.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Decision Networks for Security Risk Assessment of Critical Infrastructures

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in

            Full Access

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader
            About Cookies On This Site

            We use cookies to ensure that we give you the best experience on our website.

            Learn more

            Got it!