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Journal of Signal Processing Systems

, Volume 59, Issue 1, pp 85–93 | Cite as

Variable Length Pattern Matching for Hardware Network Intrusion Detection System

  • Chun Jason Xue
  • Meilin Liu
  • QingFeng Zhuge
  • Edwin Hsing-Mean Sha
Article
  • 152 Downloads

Abstract

With the wide adoption of internet into our everyday lives, internet security becomes an important issue. Intrusion detection at the network level is an effective way of stopping malicious attacks at the source and preventing viruses and worms from wide spreading. The key component in a successful network intrusion detection system is a high performance pattern matching engine that can uncover the malicious activities in real time. In this paper, we propose a highly parallel, scalable hardware based network intrusion detection system, that can handle variable pattern length efficiently and effectively. Pattern matching for a packet is completed in O(N log M) time where N is the size of the packet and M is the longest pattern length. Implementation is done on a standard off-the-shelf field-programmable gate array. Comparison with the other techniques shows promising results.

Keywords

Parallel system Intrusion detection 

References

  1. 1.
    Sourcefire (2003). Snort: The open source network intrusion detection system. Columbia: Sourcefire.Google Scholar
  2. 2.
    Boyer R. S., & Moore J. S. (1977). A fast string searching algorithm. Communications of the ACM, 20, 762–772.CrossRefGoogle Scholar
  3. 3.
    Anagnostakis, K. G., Antonatos, S., Markatos, E. P., & Polychronakis, M. (2003). E2xb: A domain-specific string matching algorithm for intrusion detection. In Proceedings of the 18th IFIP international security conference(SEC2003).Google Scholar
  4. 4.
    Aho, V. A., & Corasick, M. J. (1975). Efficient string matching: An aid to bibliographic search. Communications of the ACM, 18, 333–340.zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Fisk, M., & Varghese, G. (2002). An analysis of fast string matching applied to content-based forwarding and intrusion detection. In Technical report CS2001-0670 (updated version). San Diego: University of California.Google Scholar
  6. 6.
    Norton, M., & Roelker, D. (2002). Snort 2.0: Detection revised. http://www.sourcefire.com/.
  7. 7.
    Fisk, M., & Varghese, G. (2004). Applying fast string matching to intrusion detection. Technical Report CS2001-0670, UCSDGoogle Scholar
  8. 8.
    Tuck, N., Sherwood, T., Calder, B., & Varghese, G. (2004). Deterministic memory-efficient string matching algorithms for intrusion detection. In Proceedings of the 23rd conference of the IEEE communications society (Infocomm), March.Google Scholar
  9. 9.
    Aldwairi, M., Conte, T., & Franzon, P. (2005). Configurable string matching hardware for speeding up intrusion detection. In Workshop on architecture support for security and anti-virus (pp. 99–107).Google Scholar
  10. 10.
    Baker, Z. K. (2004). Time and area efficient pattern matching on fpgas. In Proceedings of the 2004 ACM/SIGDA 12th international symposium on field programmable gate arrays (pp. 223–232).Google Scholar
  11. 11.
    Knuth, D. E., Morris, J., & Pratt, V. R. (1977). Fast pattern matching in string. SIAM Journal on Computing, 6, 323–350.zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Li, S., Torresen, J., & Soraasen, O. (2003). Exploiting reconfigurable hardware for network security. In Proceedings of the 11th IEEE symposium on field-programmable custom computing machines.Google Scholar
  13. 13.
    Kruegel, C., et al. (2002). Automatic rule clustering for improved, signature based intrusion detection. Santa Barbara: University of California.Google Scholar
  14. 14.
    Tan, L., & Sherwood, T. (2005). A high throughput string matching architecture for intrusion detection and prevention. In Proceedings of the 32nd annual international symposium on computer architecture (pp. 112–122).Google Scholar
  15. 15.
    Liu, R.-T., Huang, N.-F., Chen, C.-H., & Kao, C.-N. (2004). A fast string-matching algorithm for nerwork processor-based intrusion detection system. ACM Transactions on Embedded Computing Systems, 3, 614–633CrossRefGoogle Scholar
  16. 16.
    Dharmapurikar, S., Krishnamurthy, P., Sproull, T. & Lockwood, J. (2003). Deep packet inspection using parallel bloom filters. In Proceedings of HotL.Google Scholar
  17. 17.
    Bloom, B. H. (1970). Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13, 422–426.zbMATHCrossRefGoogle Scholar
  18. 18.
    Song, H., & Lockwood, J. (2005). Multi-pattern signature matching for hardware network intrusion detection systems. In Proceedings of IEEE GLOBECOM 2005.Google Scholar
  19. 19.
    Ramakrishna, M., Fu, E., & Bahcekapili, E. (1994) A performance study of hashing functions for hardware applications. In Proceedings of internal conference on computing and information (pp. 1621–1636).Google Scholar
  20. 20.
    Gokhake, M., Dubois, D., Dubois, A., Boorman, M., Poole, S., & Hogsett, V. (2002). Towards gigabit rate network intrusion detection. In Proceeding of FPL2002.Google Scholar
  21. 21.
    Moscola, J., Lockwood, J., Loui, R. P., & Pachos, M. (2003). Implementation of a content-scanning module for an interenet firewall. In Proceeding of FCCM 2003.Google Scholar
  22. 22.
    Cho, Y. H., Navab, S., & Mangione-Smith, W. H. (2002). Specialized hardware for deep network packet filtering. In Proceeding of FPL 2002: 12th international conference on field-programmable logic and applications, Sept.Google Scholar
  23. 23.
    Xilinx, Inc. (2004). Virtex-II Pro and virtex-II Pro X platform FPGAs: Complete data sheet. San Jose: Xilinx.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Chun Jason Xue
    • 1
  • Meilin Liu
    • 2
  • QingFeng Zhuge
    • 3
  • Edwin Hsing-Mean Sha
    • 3
  1. 1.Department of Computer ScienceCity University of Hong KongKowloonHong Kong
  2. 2.Department of Computer Science and EngineeringWright State UniversityDaytonUSA
  3. 3.Department of Computer ScienceUniversity of Texas at DallasRichardsonUSA

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