Identifying Critical Attack Assets in Dependency Attack Graphs

  • Reginald E. Sawilla
  • Xinming Ou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5283)


Attack graphs have been proposed as useful tools for analyzing security vulnerabilities in network systems. Even when they are produced efficiently, the size and complexity of attack graphs often prevent a human from fully comprehending the information conveyed. A distillation of this overwhelming amount of information is crucial to aid network administrators in efficiently allocating scarce human and financial resources. This paper introduces AssetRank, a generalization of Google’s PageRank algorithm which ranks web pages in web graphs. AssetRank addresses the unique semantics of dependency attack graphs and incorporates vulnerability data from public databases to compute metrics for the graph vertices (representing attacker privileges and vulnerabilities) which reveal their importance in attacks against the system. The results of applying the algorithm on a number of network scenarios show that the numeric ranks computed are consistent with the intuitive importance that the privileges and vulnerabilities have to an attacker. The vertex ranks can be used to prioritize countermeasures, help a human reader to better comprehend security problems, and provide input to further security analysis tools.


attack graph security metric PageRank eigenvector 


  1. 1.
    Ammann, P., Wijesekera, D., Kaushik, S.: Scalable, graph-based network vulnerability analysis. In: Proceedings of 9th ACM Conference on Computer and Communications Security, Washington, DC (November 2002)Google Scholar
  2. 2.
    Ingols, K., Lippmann, R., Piwowarski, K.: Practical attack graph generation for network defense. In: 22nd Annual Computer Security Applications Conference (ACSAC), Miami Beach, Florida (December 2006)Google Scholar
  3. 3.
    Noel, S., Jajodia, S., O’Berry, B., Jacobs, M.: Efficient minimum-cost network hardening via exploit dependency graphs. In: 19th Annual Computer Security Applications Conference (ACSAC) (December 2003)Google Scholar
  4. 4.
    Ou, X., Boyer, W.F., McQueen, M.A.: A scalable approach to attack graph generation. In: 13th ACM Conference on Computer and Communications Security (CCS), pp. 336–345 (2006)Google Scholar
  5. 5.
    Phillips, C., Swiler, L.P.: A graph-based system for network-vulnerability analysis. In: NSPW 1998: Proceedings of the 1998 workshop on New security paradigms, pp. 71–79. ACM Press, New York (1998)Google Scholar
  6. 6.
    Sheyner, O., Haines, J., Jha, S., Lippmann, R., Wing, J.M.: Automated generation and analysis of attack graphs. In: Proceedings of the 2002 IEEE Symposium on Security and Privacy, pp. 254–265 (2002)Google Scholar
  7. 7.
    Noel, S., Jajodia, S.: Managing attack graph complexity through visual hierarchical aggregation. In: VizSEC/DMSEC 2004: Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, pp. 109–118. ACM Press, New York (2004)CrossRefGoogle Scholar
  8. 8.
    Noel, S., Jacobs, M., Kalapa, P., Jajodia, S.: Multiple coordinated views for network attack graphs. In: IEEE Workshop on Visualization for Computer Security (VizSEC 2005) (2005)Google Scholar
  9. 9.
    Lippmann, R., Williams, L., Ingols, K.: An interactive attack graph cascade and reachability display. In: IEEE Workshop on Visualization for Computer Security (VizSEC 2007) (2007)Google Scholar
  10. 10.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998)Google Scholar
  11. 11.
    Meyer, C.D.: Matrix analysis and applied linear algebra. Society for Industrial and Applied Mathematics. Philadelphia, PA, USA (2000)Google Scholar
  12. 12.
    Bianchini, M., Gori, M., Scarselli, F.: Inside PageRank. ACM Trans. Inter. Tech. 5(1), 92–128 (2005)CrossRefGoogle Scholar
  13. 13.
    Ellson, J., Gansner, E., Koutsofios, L., North, S., Woodhull, G.: Graphviz-Open Source Graph Drawing Tools. Graph Drawing, 483–485 (2001)Google Scholar
  14. 14.
    Sheyner, O.: Scenario Graphs and Attack Graphs. Ph.D thesis, Carnegie Mellon (April 2004)Google Scholar
  15. 15.
    Swiler, L.P., Phillips, C., Ellis, D., Chakerian, S.: Computer-attack graph generation tool. In: DARPA Information Survivability Conference and Exposition (DISCEX II 2001), June 2001, vol. 2 (2001)Google Scholar
  16. 16.
    Mehta, V., Bartzis, C., Zhu, H., Clarke, E., Wing, J.: Ranking attack graphs. In: Proceedings of Recent Advances in Intrusion Detection (RAID) (September 2006)Google Scholar
  17. 17.
    Wang, L., Singhal, A., Jajodia, S.: Measuring network security using attack graphs. In: Third Workshop on Quality of Protection (QoP) (2007)Google Scholar
  18. 18.
    Dewri, R., Poolsappasit, N., Ray, I., Whitley, D.: Optimal security hardening using multi-objective optimization on attack tree models of networks. In: 14th ACM Conference on Computer and Communications Security (CCS) (2007)Google Scholar
  19. 19.
    Jha, S., Sheyner, O., Wing, J.M.: Two formal analyses of attack graphs. In: Proceedings of the 15th IEEE Computer Security Foundations Workshop, Nova Scotia, Canada, June 2002, pp. 49–63 (2002)Google Scholar
  20. 20.
    Homer, J., Varikuti, A., Ou, X., McQueen, M.A.: Improving attack graph visualization through data reduction and attack grouping. In: The 5th International Workshop on Visualization for Cyber Security (VizSEC) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Reginald E. Sawilla
    • 1
  • Xinming Ou
    • 2
  1. 1.Defence Research and Development CanadaOttawaCanada
  2. 2.Kansas State UniversityManhattanUSA

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