skip to main content
10.1145/3230833.3232817acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaresConference Proceedingsconference-collections
research-article

Attack Difficulty Metric for Assessment of Network Security

Authors Info & Claims
Published:27 August 2018Publication History

ABSTRACT

In recent days, organizational networks are becoming target of sophisticated multi-hop attacks. Attack Graph has been proposed as a useful modeling tool for complex attack scenarios by combining multiple vulnerabilities in causal chains. Analysis of attack scenarios enables security administrators to calculate quantitative security measurements. These measurements justify security investments in the organization. Different security metrics based on attack graph have been introduced for evaluation of comparable security measurements. Studies show that difficulty of exploiting the same vulnerability changes with change of its position in the causal chains of attack graph. In this paper, a new security metric based on attack graph, namely Attack Difficulty has been proposed to include this position factor. The security metrics are classified in two major categories viz. counting metrics and difficulty-based metrics. The proposed Attack Difficulty Metric employs both categories of metrics as the basis for its measurement. Case studies have been presented for demonstrating applicability of the proposed metric. Comparison of this new metric with other attack graph based security metrics has also been included to validate its acceptance in real life situations.

References

  1. P. Ammann, D. Wijesekera, and S. Kaushik. 2002. Scalable, graph-based network vulnerability analysis. In 9th ACM conference on Computer and communications security, pages 217--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. N. Ghosh and S. K. Ghosh. 2009, An approach for security assessment of network configurations using attack graph, in Networks and Communications, 2009. NETCOM'09. First International Conference on, pp. 283--288. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. N. Idika and B. Bhargava. 2012. Extending attack graph-based security metrics and aggregating their application. IEEE Transactions on Dependable and Secure Computing, 9:75--85. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Singhal and X. Ou. 2011, Security Risk Analysis of Enterprise Networks Using Probabilistic Attack Graphs, Nat. Inst. Sci. Technol., Gaithersburg, MD, USA, Nat. Inst. Sci. Technol. Interagency Rep. 7788.Google ScholarGoogle Scholar
  5. G. S. Bopche, B. M. Mehtre. 2017. Graph Similarity Metrics for Assessing Temporal Changes in Attack Surface of Dynamic Networks. In Computer Security, vol. 64, no. C, pp. 16--43. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. FIRST. 2015. Forum of Incident Response and Security Teams: Common Vulnerability Scoring System (CVSS) Version 3.0. Retrieved from https://www.first.org/cvss.Google ScholarGoogle Scholar
  7. S. Noel and S. Jajodia. 2014. Metrics suite for network attack graph analytics. In Proc. CISR'14. 5--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. Pamula, P. Ammann, S. Jajodia, and V. Swarup. 2006. A weakest-adversary security metric for network configuration security analysis. In ACM 2nd Workshop on Quality of Protection, Alexandria, VA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. Wang, A. Singhal, and S. Jajodia. 2007. Measuring the overall security of network configurations using attack graphs. In Proceedings of the 21st Annual IFIP WG 11.3 Working Conference on Data and Application Security, pages 98--112, Redondo Beach, CA,. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. L. Wang, T. Islam, T. Long, A. Singhal, and S. Jajodia. 2008. An attack graph-based probabilistic security metric. In Proceedings of the 22nd IFIP DBSec. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. N. Fenton and J. Bieman. 2014, Software Metrics A Rigorous & Practical Approach, CRC Press, 3rd edition. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Frigault, L. Wang, A. Singhal, and S. Jajodia. 2008. Measuring network security using dynamic Bayesian network. In Proceedings of the 14th ACM Workshop on Quality of Protection, Alexandria, VA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. N. Poolsappasit, R. Dewri, and I. Ray. 2012. Dynamic security risk management using bayesian attack graphs. IEEE Trans. Dependable Secur. Comput, 9(1):61--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. W. Li and R. Vaughn. 2006, Cluster Security Research Involving the Modeling of Network Exploitations Using Exploitation Graphs, Proc. Sixth IEEE Int'l Symp. Cluster Computing and Grid Workshops. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. X. Ou, S. Govindavajhala, and A. W. Appel. 2005. Mulval: a logic-based network security analyzer. In Proceedings of the 14th conference on USENIX Security Symposium, pages 113--128, Baltimore, MD. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. K. Ingols, M. Chu, R. Lippmann, S. Webster, and S. Boyer. 2009. Modeling modern network attacks and countermeasures using attack graphs. In 2009 Annual Computer Sec. Applic. Conf. (ACSAC), Austin, TX, pp. 117--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. C. Phillips and L. Swiler. 1998. A graph-based system for network-vulnerability analysis. In Proceedings of the New Security Paradigms Workshop, pages 71--79, Charlottesville, VA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. O. Sheyner, J. Haines, S. Jha, R. Lippmann, and J. M. Wing. 2002. Automated generation and analysis of attack graphs. In Proceedings of the 2002 IEEE Symposium on Security and Privacy, pages 254--265. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. L. Munoz-Gonzalez, D. Sgandurra, A. Paudice, and E. C. Lupu. 2017. Efficient attack graph analysis through approximate inference. In ACM Transactions on Privacy and Security (TOPS), Vol. 20(3). Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. R. Ortalo, Y. Deswarte, and M. Kaâniche. 1999. Experimenting with quantitative evaluation tools for monitoring operational security. IEEE Trans. Software Eng., 25(5):633--650. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. R. Lippmann, K. Ingols, C. Scott, K. Piwowarski, K. Kratkiewicz, M. Artz, and R. Cunningham. 2006. Validating and restoring defense in depth using attack graphs. In MILCOM 2006, Washington, DC, U.S.A. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. H. Holm and K. K. Afridi. 2015. An expert-based investigation of the common vulnerability scoring system. Computers & Security; 53:18--30 Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. A. Jaquith. 2007. Security metrics: replacing fear, uncertainty, and doubt. Addis on-Wesley Professional. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. W. Jansen. 2009. Directions in security metrics research. NISTIR 7564. U.S. National Institute of Standards and Technology.Google ScholarGoogle Scholar
  25. SSE-CMM: Systems Security Engineering Capability Maturity Model, International Systems Security Engineering Association (ISSEA).Google ScholarGoogle Scholar
  26. L. Wang, S. Jajodia, A. Singhal, P. Cheng, S. Noel. 2014, k-zero day safety: A network security metric for measuring the risk of unknown vulnerabilities, IEEE Trans. Depend. Sec. Comput., vol. 11, no. 1, pp. 30--44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. National vulnerability database. available at: http://www.nvd.org.Google ScholarGoogle Scholar
  28. The MITRE Corporation. Common weakness scoring system 2010. http://cwe.mitre.org/cwss/.Google ScholarGoogle Scholar
  29. M. Dacier, Y. Deswarte and M. Kaaniche. 1996, Quantitative Assessment of Operational Security: Models and Tools, LAAS Research Report 96493.Google ScholarGoogle Scholar

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
  • Published in

    cover image ACM Other conferences
    ARES '18: Proceedings of the 13th International Conference on Availability, Reliability and Security
    August 2018
    603 pages
    ISBN:9781450364485
    DOI:10.1145/3230833

    Copyright © 2018 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 27 August 2018

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    ARES '18 Paper Acceptance Rate128of260submissions,49%Overall Acceptance Rate228of451submissions,51%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader