Advertisement

Assessing Attack Impact on Business Processes by Interconnecting Attack Graphs and Entity Dependency Graphs

  • Chen Cao
  • Lun-Pin Yuan
  • Anoop Singhal
  • Peng Liu
  • Xiaoyan Sun
  • Sencun Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10980)

Abstract

Cyber-defense and cyber-resilience techniques sometimes fail in defeating cyber-attacks. One of the primary causes is the ineffectiveness of business process impact assessment in the enterprise network. In this paper, we propose a new business process impact assessment method, which measures the impact of an attack towards a business-process-support enterprise network and produces a numerical score for this impact. The key idea is that all attacks are performed by exploiting vulnerabilities in the enterprise network. So the impact scores for business processes are the function result of the severity of the vulnerabilities and the relations between vulnerabilities and business processes. This paper conducts a case study systematically and the result shows the effectiveness of our method.

Notes

Acknowledgment

We thank the anonymous reviewers for their valuable comments. This work was supported by NIST 60NANB17D279, NSF CNS-1505664, ARO W911NF-13-1-0421 (MURI), and NSF CNS-1618684.

Disclaimer. This paper is not subject to copyright in the United States. Commercial products are identified in order to adequately specify certain procedures. In no case does such identification imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the identified products are necessarily the best available for the purpose.

References

  1. 1.
    Ammann, P., Wijesekera, D., Kaushik, S.: Scalable, graph-based network vulnerability analysis. In: Proceedings of the 9th ACM Conference on Computer and Communications Security, pp. 217–224. ACM (2002)Google Scholar
  2. 2.
    Chen, X., Zhang, M., Mao, Z.M., Bahl, P.: Automating network application dependency discovery: experiences, limitations, and new solutions. In: OSDI, vol. 8, pp. 117–130 (2008)Google Scholar
  3. 3.
    Dai, J., Sun, X., Liu, P., Giacobe, N.: Gaining big picture awareness through an interconnected cross-layer situation knowledge reference model. In: 2012 International Conference on Cyber Security (CyberSecurity), pp. 83–92. IEEE (2012)Google Scholar
  4. 4.
    Dewri, R., Poolsappasit, N., Ray, I., Whitley, D.: Optimal security hardening using multi-objective optimization on attack tree models of networks. In: Proceedings of the 14th ACM Conference on Computer and Communications Security, pp. 204–213. ACM (2007)Google Scholar
  5. 5.
    Frigault, M., Wang, L.: Measuring network security using Bayesian network-based attack graphs. In: Proceedings of the 2008 32nd Annual IEEE International Computer Software and Applications Conference, pp. 698–703. IEEE Computer Society (2008)Google Scholar
  6. 6.
    Frigault, M., Wang, L., Singhal, A., Jajodia, S.: Measuring network security using dynamic Bayesian network. In: Proceedings of the 4th ACM Workshop on Quality of Protection, pp. 23–30. ACM (2008)Google Scholar
  7. 7.
    Jajodia, S., Noel, S., O’Berry, B.: Topological analysis of network attack vulnerability. In: Kumar, V., Srivastava, J., Lazarevic, A. (eds.) Managing Cyber Threats. Massive Computing, vol. 5, pp. 247–266. Springer, Boston, MA (2005).  https://doi.org/10.1007/0-387-24230-9_9
  8. 8.
    Jakobson, G.: Mission cyber security situation assessment using impact dependency graphs. In: 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2011)Google Scholar
  9. 9.
  10. 10.
    Noel, S., Jajodia, S., O’Berry, B., Jacobs, M.: Efficient minimum-cost network hardening via exploit dependency graphs. In: 2003 Proceedings of 19th Annual Computer Security Applications Conference, pp. 86–95. IEEE (2003)Google Scholar
  11. 11.
    Ou, X., Boyer, W.F., McQueen, M.A.: A scalable approach to attack graph generation. In: Proceedings of the 13th ACM Conference on Computer and Communications Security, pp. 336–345. ACM (2006)Google Scholar
  12. 12.
    Phillips, C., Swiler, L.P.: A graph-based system for network-vulnerability analysis. In: Proceedings of the 1998 Workshop on New Security Paradigms, pp. 71–79. ACM (1998)Google Scholar
  13. 13.
    Poolsappasit, N., Dewri, R., Ray, I.: Dynamic security risk management using Bayesian attack graphs. IEEE Trans. Dependable Sec. Comput. 9(1), 61–74 (2012)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Ray, I., Poolsapassit, N.: Using attack trees to identify malicious attacks from authorized insiders. In: di Vimercati, S.C., Syverson, P., Gollmann, D. (eds.) ESORICS 2005. LNCS, vol. 3679, pp. 231–246. Springer, Heidelberg (2005).  https://doi.org/10.1007/11555827_14CrossRefGoogle Scholar
  16. 16.
    Saripalli, P., Walters, B.: QUIRC: a quantitative impact and risk assessment framework for cloud security. In: 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD), pp. 280–288. IEEE (2010)Google Scholar
  17. 17.
    Sheyner, O., Haines, J., Jha, S., Lippmann, R., Wing, J.M.: Automated generation and analysis of attack graphs. In: 2002 Proceedings of IEEE Symposium on Security and Privacy, pp. 273–284. IEEE (2002)Google Scholar
  18. 18.
    Sun, X., Singhal, A., Liu, P.: Who touched my mission: towards probabilistic mission impact assessment. In: Proceedings of the 2015 Workshop on Automated Decision Making for Active Cyber Defense, pp. 21–26. ACM (2015)Google Scholar
  19. 19.
    Sun, X., Singhal, A., Liu, P.: Towards actionable mission impact assessment in the context of cloud computing. In: Livraga, G., Zhu, S. (eds.) DBSec 2017. LNCS, vol. 10359, pp. 259–274. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-61176-1_14CrossRefGoogle Scholar
  20. 20.
    Sun, Y., Wu, T.Y., Liu, X., Obaidat, M.S.: Multilayered impact evaluation model for attacking missions. IEEE Syst. J. 10(4), 1304–1315 (2016)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Chen Cao
    • 1
  • Lun-Pin Yuan
    • 1
  • Anoop Singhal
    • 2
  • Peng Liu
    • 1
  • Xiaoyan Sun
    • 3
  • Sencun Zhu
    • 1
  1. 1.The Pennsylvania State UniversityHarrisburgUSA
  2. 2.National Institute of Standards and TechnologyGaithersburgUSA
  3. 3.California State UniversitySacramentoUSA

Personalised recommendations