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Generating Simplified Regular Expression Signatures for Polymorphic Worms

  • Yong Tang
  • Xicheng Lu
  • Bin Xiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4610)

Abstract

It is crucial to automatically generate accurate and effective signatures to defense against polymorphic worms. Previous work using conjunctions of tokens or token subsequence could lose some important information, like ignoring 1 byte token and neglecting the distances in the sequential tokens. In this paper we propose the Simplified Regular Expression (SRE) signature, and present its signature generation method based on the multiple sequence alignment algorithm. The multiple sequence alignment algorithm is extended from the pairwise sequence alignment algorithm, which encourages the contiguous substring extraction and is able to support wildcard string alignment and to preserve the distance of invariant content segment in generated SRE signatures. Thus, the generated SRE signature can express distance information for invariant content in polymorphic worms, which in turn makes even 1 byte invariant content extracted from polymorphic worms become valuable. Experiments on several types of polymorphic worms show that, compared with signatures generated by current network-based signature generation systems (NSGs), the generated SRE signatures are more accurate and precise to match polymorphic worms.

Keywords

Intrusion Detection Regular Expression Alignment Result Pairwise Sequence Alignment Sequence Alignment Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Kreibich, C., Crowcroft, J.: Honeycomb - creating intrusion detection signatures using honeypots. In: Proceedings of the Second Workshop on Hot Topics in Networks (Hotnets II), Boston (November 2003)Google Scholar
  2. 2.
    Kim, H.A., Karp, B.: Autograph: Toward automated, distributed worm signature detection. In: USENIX Security Symposium, pp. 271–286 (2004)Google Scholar
  3. 3.
    Singh, S., Estan, C., Varghese, G., Savage, S.: Automated worm fingerprinting. In: Proc. 6th USENIX OSDI, San Francisco, CA (December 2004)Google Scholar
  4. 4.
    Newsome, J., Karp, B., Song, D.: Polygraph: Automatically generating signatures for polymorphic worms. In: Proceedings of the 2005 IEEE Symposium on Security and Privacy, pp. 226–241. IEEE Computer Society Press, Washington (2005)Google Scholar
  5. 5.
    Crandall, J.R., Su, Z., Wu, S.F., Chong, F.T.: On deriving unknown vulnerabilities from zero-day polymorphic and metamorphic worm exploits. In: Proceedings of the 12th ACM conference on Computer and communications security, pp. 235–248. ACM Press, New York (2005)CrossRefGoogle Scholar
  6. 6.
    Li, Z., Sanghi, M., Chen, Y., Kao, M.-Y., Chavez, B.: Hamsa: Fast signature generation for zero-day polymorphic worms with provable attack resilience. In: Proceedings of the 2006 IEEE Symposium on Security and Privacy (S&P 2006), IEEE Computer Society Press, Washington (2006)Google Scholar
  7. 7.
    Newsome, J., Song, D.: Dynamic taint analysis for automatic detection, analysis, and signaturegeneration of exploits on commodity software. In: NDSS (2005)Google Scholar
  8. 8.
    Liang, Z., Sekar, R.: Fast and automated generation of attack signatures: a basis for building self-protecting servers. In: CCS 2005: Proceedings of the 12th ACM conference on Computer and communications security, pp. 213–222. ACM Press, New York (2005)CrossRefGoogle Scholar
  9. 9.
    Xu, J., Ning, P., Kil, C., Zhai, Y., Bookholt, C.: Automatic diagnosis and response to memory corruption vulnerabilities. In: CCS 2005: Proceedings of the 12th ACM conference on Computer and communications security, pp. 223–234. ACM Press, New York (2005)CrossRefGoogle Scholar
  10. 10.
    Wang, X., Li, Z., Xu, J., Reiter, M.K., Kil, C., Choi, J.Y.: Packet vaccine: black-box exploit detection and signature generation. In: CCS 2006: Proceedings of the 13th ACM conference on Computer and communications security, pp. 37–46. ACM Press, New York (2006)CrossRefGoogle Scholar
  11. 11.
    Sommer, R., Paxson, V.: Enhancing byte-level network intrusion detection signatures with context. In: CCS 2003: Proceedings of the 10th ACM conference on Computer and communications security, pp. 262–271. ACM Press, New York (2003)CrossRefGoogle Scholar
  12. 12.
    Kumar, S., Dharmapurikar, S., Yu, F., Crowley, P., Turner, J.: Algorithms to accelerate multiple regular expressions matching for deep packet inspection. In: Proceedings of ACM SIGCOMM 2006, vol. 36, pp. 339–350. ACM Press, New York (2006)Google Scholar
  13. 13.
    Tang, Y., Chen, S.: Defending against internet worms: A signature-based approach. In: Proceedings of the 24th Annual Conference IEEE INFOCOM 2005 (March 2005)Google Scholar
  14. 14.
    Gelfand, M.S., Mironov, A., Pevzner, P.: Gene recognition via splices sequence alignment. In: Proc. Natl. Acad. Sci. USA, pp. 9061–9066 (1996)Google Scholar
  15. 15.
    Goad, W.B., Kanehisa, M.I.: Pattern recognition in nucleic acid sequences: a general method for finding local homologies and symmetries. Nucleic Acids Research 10, 247–263 (1982)CrossRefGoogle Scholar
  16. 16.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 443–453 (1970)CrossRefGoogle Scholar
  17. 17.
    Lippmann, R., Haines, J.W., Fried, D.J., Korba, J., Das, K.: The 1999 darpa off-line intrusion detection evaluation. Comput. Networks 34(4), 579–595 (2000)CrossRefGoogle Scholar
  18. 18.
    Yegneswaran, V., Giffin, J.T., Barford, P., Jha, S.: An Architecture for Generating Semantics-Aware Signatures. In: Proceedings of the 14th USENIX Security Symposium, Baltimore, MD, USA, pp. 97–112 (August 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yong Tang
    • 1
  • Xicheng Lu
    • 1
  • Bin Xiao
    • 2
  1. 1.College of Computer, National University of Defense Technology, Changsha Hunan, 410073P.R. China
  2. 2.Department of Computing, Hong Kong Polytechnic University, Hong Kong 

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