Skip to main content

On the Limits of Payload-Oblivious Network Attack Detection

  • Conference paper
Recent Advances in Intrusion Detection (RAID 2008)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 5230))

Included in the following conference series:

Abstract

We introduce a methodology for evaluating network intrusion detection systems using an observable attack space, which is a parameterized representation of a type of attack that can be observed in a particular type of log data. Using the observable attack space for log data that does not include payload (e.g., NetFlow data), we evaluate the effectiveness of five proposed detectors for bot harvesting and scanning attacks, in terms of their ability (even when used in conjunction) to deter the attacker from reaching his goals. We demonstrate the ranges of attack parameter values that would avoid detection, or rather that would require an inordinately high number of false alarms in order to detect them consistently.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Alata, E., Nicomette, V., Kaaniche, M., Dacier, M., Herrb, M.: Lessons learned from the deployment of a high-interaction honeypot. In: Proceedings of the 2006 European Dependable Computing Conference (2006)

    Google Scholar 

  2. Axelsson, S.: The base rate fallacy and the difficulty of intrusion detection. ACM Transactions on Information and System Security 3(3), 186–205 (2000)

    Article  MathSciNet  Google Scholar 

  3. Binkley, J.: An algorithm for anomaly-based botnet detection. In: Proceedings of the 2006 USENIX Workshop on Steps for Reducing Unwanted Traffic on the Internet (SRUTI) (2006)

    Google Scholar 

  4. Cárdenas, A., Baras, J., Seamon, K.: A framework for evaluation of intrusion detection systems. In: Proceedings of the 2006 IEEE Symposium on Security and Privacy (2006)

    Google Scholar 

  5. Claffy, K., Braun, H., Polyzos, G.: A parameterizable methodology for internet traffic flow profiling. IEEE Journal on Selected Areas in Communications 13(8), 1481–1494 (1995)

    Article  Google Scholar 

  6. Collins, M.P., Reiter, M.: Hit-list worm detection and bot identification in large networks using protocol graphs. In: Kruegel, C., Lippmann, R., Clark, A. (eds.) RAID 2007. LNCS, vol. 4637, pp. 276–295. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Collins, M.P., Reiter, M.K.: Anomaly detection amidst constant anomalies: Training IDS on constantly attacked data. Technical Report CMU-CYLAB-08-006, Carnegie Mellon University, CyLab (2008)

    Google Scholar 

  8. Gaffney, J., Ulvila, J.: Evaluation of intrusion detectors: A decision theory approach. In: Proceedings of the 2001 IEEE Symposium on Security and Privacy (2001)

    Google Scholar 

  9. Gates, C., Taylor, C.: Challenging the anomaly detection paradigm, a provocative discussion. In: Proceedings of the 2006 New Security Paradigms Workshop, pp. 22–29 (2006)

    Google Scholar 

  10. Jung, J.: Real-Time Detection of Malicious Network Activity Using Stochastic Models. PhD thesis, Massachuesetts Institute of Technology (2006)

    Google Scholar 

  11. Jung, J., Paxson, V., Berger, A.W., Balakrishnan, H.: Fast portscan detection using sequential hypothesis testing. In: Proceedings of the 2004 IEEE Symposium on Security and Privacy (2004)

    Google Scholar 

  12. Kang, M., Caballero, J., Song, D.: Distributed evasive scan techniques and countermeasures. In: Hämmerli, B.M., Sommer, R. (eds.) DIMVA 2007. LNCS, vol. 4579, pp. 157–174. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  13. Killourhy, K., Maxion, R., Tan, K.: A defense-centric taxonomy based on attack manifestations. In: Proceedings of the 2004 Conference on Dependable Systems and Networks (DSN) (2004)

    Google Scholar 

  14. Kreyszig, E.: Advanced Engineering Mathematics, 9th edn. J. Wiley and Sons, Chichester (2005)

    Google Scholar 

  15. Lakhina, A., Crovella, M., Diot, C.: Mining anomalies using traffic feature distributions. In: Proceedings of the 2005 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM), pp. 217–228 (2005)

    Google Scholar 

  16. Lippmann, R., Fried, D., Graf, I., Haines, J., Kendall, K., McClung, D., Weber, D., Webster, S., Wyschogrod, D., Cunningham, R., Zissman, M.: Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation. In: Proceedings of the DARPA Information Survivability Conference and Exposition (2000)

    Google Scholar 

  17. Maxion, R., Tan, K.: Benchmarking anomaly-based detection systems. In: Proceedings of the 2000 Conference on Dependable Systems and Networks (DSN) (2000)

    Google Scholar 

  18. McHugh, J.: Testing intrusion detection systems: A critique of the 1998 and 1998 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory. ACM Transactions on Information and Systems Security 3(4), 262–294 (2000)

    Article  Google Scholar 

  19. Northcutt, S.: Network Intrusion Detection: An Analyst’s Handbook. New Riders (1999)

    Google Scholar 

  20. Paxson, V.: Bro: A system for detection network intruders in real time. In: Proceedings of the 2008 Usenix Security Symposium (1998)

    Google Scholar 

  21. Sekar, V., Xie, Y., Reiter, M.K., Zhang, H.: A multi-resolution approach for worm detection and containment. In: Proceedings of the 36th International Conference on Dependable Systems and Networks, June 2006, pp. 189–198 (2006)

    Google Scholar 

  22. Shapiro, S., Wilk, M.: An analysis of variance test for normality (complete samples). Biometrika 52(3–4), 591–611 (1965)

    MATH  MathSciNet  Google Scholar 

  23. Staniford-Chen, S., Cheung, S., Crawford, R., Dilger, M., Frank, J., Hoagland, J., Levitt, K., Wee, C., Yip, R., Zerkle, D.: GrIDS – A graph-based intrusion detection system for large networks. In: Proceedings of the 19th National Information Systems Security Conference, pp. 361–370 (1996)

    Google Scholar 

  24. Stolfo, S., Fan, W., Lee, W., Prodromidis, A., Chan, P.: Cost-based modeling for fraud and intrusion detection: Results from the JAM project. In: Proceedings of the 2000 DARPA Information Survivability Conference and Exposition (2000)

    Google Scholar 

  25. Tan, K., Maxion, R.: The effects of algorithmic diversity on anomaly detector performance. In: Proceedings of the 2005 Conference on Dependable Systems and Networks (DSN) (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Richard Lippmann Engin Kirda Ari Trachtenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Collins, M.P., Reiter, M.K. (2008). On the Limits of Payload-Oblivious Network Attack Detection. In: Lippmann, R., Kirda, E., Trachtenberg, A. (eds) Recent Advances in Intrusion Detection. RAID 2008. Lecture Notes in Computer Science, vol 5230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87403-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87403-4_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87402-7

  • Online ISBN: 978-3-540-87403-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics