Journal in Computer Virology

, Volume 4, Issue 3, pp 197–210 | Cite as

On the adaptive real-time detection of fast-propagating network worms

Original Paper

Abstract

We present two light-weight worm detection algorithms that offer significant advantages over fixed-threshold methods. The first algorithm, rate-based sequential hypothesis testing (RBS), aims at the large class of worms that attempts to quickly propagate, thus exhibiting abnormal levels of the rate at which hosts initiate connections to new destinations. The foundation of RBS derives from the theory of sequential hypothesis testing, the use of which for detecting randomly scanning hosts was first introduced by our previous work developing TRW (Jung et al. in Proceedings of the IEEE Symposium on Security and Privacy, 9–12 May 2004). The sequential hypothesis testing methodology enables us to engineer detectors to meet specific targets for false-positive and false-negative rates, rather than triggering when fixed thresholds are crossed. In this sense, the detectors that we introduce are truly adaptive. We then introduce RBS+TRW, an algorithm that combines fan-out rate (RBS) and probability of failure (TRW) of connections to new destinations. RBS+TRW provides a unified framework that at one end acts as pure RBS and at the other end as pure TRW. Selecting an operating point that includes both mechanisms extends RBS’s power in detecting worms that scan randomly selected IP addresses. Using four traces from three qualitatively different sites, we evaluate RBS and RBS+TRW in terms of false positives, false negatives, and detection speed, finding that RBS+TRW provides good detection of actual Code Red worm outbreaks that we caught in our trace as well as internal Web crawlers that we use as proxies for targeting worms. In doing so, RBS+TRW generates fewer than one false alarm per hour for wide range of parameter choices.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Nmap—free security scanner for network exploration & security audits. http://www.insecure.org/nmap/
  2. 2.
    Chen, S., Tang, Y.: Slowing down internet worms. In: Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS’04), Tokyo, Japan, March 2004Google Scholar
  3. 3.
    Ehtereal.com. Ethereal. http://www.ethereal.com/
  4. 4.
    Eichin, M.W., Rochlis, J.A.: With microscope and tweezers: an analysis of the Internet virus of November 1988. In: Proceedings of the IEEE Symposium on Research in Security and Privacy (1989)Google Scholar
  5. 5.
    F-Secure: F-Secure Virus Descriptions: Santy. http://www.f-secure.com/v-descs/santy_a.shtml
  6. 6.
    Jung, J., Paxson, V., Berger, A.W., Balakrishnan, H.: Fast portscan detection using sequential hypothesis testing. In: Proceedings of the IEEE Symposium on Security and Privacy, 9–12 May 2004Google Scholar
  7. 7.
    Karagiannis T., Papagiannaki K. and Faloutsos M. (2005). Blinc: multilevel traffic classification in the dark. SIGCOMM Comput. Commun. Rev. 35(4): 229–240 CrossRefGoogle Scholar
  8. 8.
    Kim, H.-A., Karp, B.: Autograph: toward automated distributed worm signature detection. In: Proceedings of the 13th USENIX Security Symposium, August 9–13 (2004)Google Scholar
  9. 9.
    Schechter, S.E., Jung, J., Berger, A.W.: Fast detection of scanning worm infections. In: Proceedings of the Seventh International Symposium on Recent Advances in Intrusion Detection (RAID 2004), September 15–17 (2004)Google Scholar
  10. 10.
    Singh, S., Estan, C., Varghese, G., Savage, S.: Automated worm fingerprinting. In: Proceedings of the 13th Operating Systems Design and Implementation OSDI (December 2004)Google Scholar
  11. 11.
    Spafford, E.H.: A failure to learn from the past. In: Proceedings of the 19th Annual Computer Security Applications Conference, pp. 217–233, December 8–12 (2003)Google Scholar
  12. 12.
    Staniford, S., Paxson, V., Weaver, N.: How to own the Internet in your spare time. In: Proceedings of the 11th USENIX Security Symposium (Berkeley, CA, USA), pp. 149–170. USENIX Association, August 5–9 (2002)Google Scholar
  13. 13.
    Turkey, J.W.: A survey of sampling from contaminated distributions. In: Contributions to Probability and Statistics. Stanford University Press (1960)Google Scholar
  14. 14.
    Twycross, J., Williamson, M.M.: Implementing and testing a virus throttle. In: Proceedings of the 12th USENIX Security Symposium, August 4–8 (2003)Google Scholar
  15. 15.
    Wald A. (1947). Sequential Analysis. Wiley, New York MATHGoogle Scholar
  16. 16.
    Wang, K., Cretu, G., Stolfo, S.J.: Anomalous payload-based worm detection and signature generation. In: Proceedings of the Eighth International Symposium on Recent Advances in Intrusion Detection (RAID 2005) (September 2005)Google Scholar
  17. 17.
    Weaver, N., Paxson, V., Staniford, S., Cunningham, R.: A taxonomy of computer worms. In: Proceedings of the 2003 ACM Workshop on Rapid Malcode, pp. 11–18. ACM Press, New York, October 27 (2003)Google Scholar
  18. 18.
    Weaver, N., Staniford, S., Paxson, V.: Very fast containment of scanning worms. In: Proceedings of the 13th USENIX Security Symposium, August 9–13 (2004)Google Scholar
  19. 19.
    Whyte, D., Kranakis, E., van Oorschot, P.: DNS-based detection of scanning worms in an enterprise network. In: Proceedings of the Network and Distributed System Security Symposium (NDSS’05) (February 2005)Google Scholar
  20. 20.
    Williamson, M.M.: Throttling viruses: restricting propagation to defeat malicious mobile code. In: Proceedings of the 18th Annual Computer Security Applications Conference (ACSAC 2002), December 9–13 (2002)Google Scholar
  21. 21.
    Wong, C., Bielski, S., Studer, A., Wang, C.: Empirical analysis of rate limiting mechanisms. In: Proceedings of the Eighth International Symposium on Recent Advances in Intrusion Detection (RAID 2005) (September 2005)Google Scholar

Copyright information

© Springer-Verlag France 2007

Authors and Affiliations

  • Jaeyeon Jung
    • 1
  • Rodolfo A. Milito
    • 2
  • Vern Paxson
    • 3
    • 4
  1. 1.Intel ResearchSeattleUSA
  2. 2.Consentry NetworksMilpitasUSA
  3. 3.International Computer Science InstituteBerkeleyUSA
  4. 4.Lawrence Berkeley National LaboratoryBerkeleyUSA

Personalised recommendations