skip to main content
research-article

NetShield: massive semantics-based vulnerability signature matching for high-speed networks

Published:30 August 2010Publication History
Skip Abstract Section

Abstract

Accuracy and speed are the two most important metrics for Network Intrusion Detection/Prevention Systems (NIDS/NIPSes). Due to emerging polymorphic attacks and the fact that in many cases regular expressions (regexes) cannot capture the vulnerability conditions accurately, the accuracy of existing regex-based NIDS/NIPS systems has become a serious problem. In contrast, the recently-proposed vulnerability signatures (a.k.a data patches) can exactly describe the vulnerability conditions and achieve better accuracy. However, how to efficiently apply vulnerability signatures to high speed NIDS/NIPS with a large ruleset remains an untouched but challenging issue.

This paper presents the first systematic design of vulnerability signature based parsing and matching engine, NetShield, which achieves multi-gigabit throughput while offering much better accuracy. Particularly, we made the following contributions: (i) we proposed a candidate selection algorithm which efficiently matches thousands of vulnerability signatures simultaneously requiring a small amount of memory; (ii) we proposed an automatic lightweight parsing state machine achieving fast protocol parsing. Experimental results show that the core engine of NetShield achieves at least 1.9+Gbps signature matching throughput on a 3.8GHz single-core PC, and can scale-up to at least 11+Gbps under a 8-core machine for 794 HTTP vulnerability signatures.

References

  1. 1998 DARPA Intrusion Detection Evaluation Data Set. www.ll.mit.edu/mission/communications/ist/corpora/ideval/data/1998data.html.Google ScholarGoogle Scholar
  2. Conficker. http://en.wikipedia.org/wiki/Conficker.Google ScholarGoogle Scholar
  3. DAG card. http://www.endace.com/dag-8.1sx.html.Google ScholarGoogle Scholar
  4. NetShield Website. http://www.nshield.org.Google ScholarGoogle Scholar
  5. PRX Traffic Manager. http://www.ipoque.com/products/prx-traffic-manager.Google ScholarGoogle Scholar
  6. F. Baboescu and G. Varghese. Scalable packet classification. In proc. of ACM SIGCOMM, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Becchi and P. Crowley. A hybrid finite automaton for practical deep packet inspection. In Proc. of ACM CoNEXT, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Becchi and P. Crowley. Efficient regular expression evaluation: Theory to practice. In Proc. of IEEE/ACM ANCS, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. N. Borisov, D. J. Brumley, H. J. Wang, J. Dunagan, P. Joshi, and C. Guo. A generic application-level protocol analyzer and its language. In proc. of NDSS, 2007.Google ScholarGoogle Scholar
  10. D. Brumley, J. Newsome, D. Song, H. Wang, and S. Jha. Towards automatic generation of vulnerability-based signatures. In Proc. of IEEE Security and Privacy Symposium, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. B. Chazelle. Lower bounds for orthogonal range searching. ii: The arithmetic model. Journal of the ACM, 37(3):439--463, July 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Costa, J. Crowcroft, M. Castro, A. Rowstron, L. Zhou, L. Zhang, and P. Barham. Vigilante: End-to-end containment of internet worms. In Proc. of ACM SOSP, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. W. Cui, M. Peinado, H. J. Wang, and M. Locasto. Shieldgen: Automated data patch generation for unknown vulnerabilities with informed probing. In proc. of IEEE Security and Privacy, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. S. Dharmapurikar and V. Paxson. Robust tcp stream reassembly in the presence of adversaries. In Proc. USENIX Security Symposium, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. P. Gupta and N. McKeown. Packet classification on multiple fields. In proc. of ACM SIGCOMM, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. P. Gupta and N. McKeown. Classification using hierarchical intelligent cuttings. IEEE Micro, 20(1):34--41, Jan 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. S. Kumar, S. Dharmapurikar, F. Yu, P. Crowley, and J. Turner. Algorithms to accelerate multiple regular expression matching for deep packet inspection. In Proc. of ACM SIGCOMM, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. T. V. Lakshman and D. Stiliadis. High-speed policy-based packet forwarding using efficient multi-dimensional range matching. In proc. of ACM SIGCOMM, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Z. Li, X. Gao, Y. Chen, and B. Liu. Netshield: Matching with a large vulnerability signature ruleset for high performance network defense. Technical Report NWU-EECS-08-07, Northwestern University, 2009.Google ScholarGoogle Scholar
  20. R. Pang, V. Paxson, R. Sommer, and L. Peterson. binpac: A yacc for writing application protocol parsers. In proc. Of ACM IMC, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. V. Paxson. Bro: A system for detecting network intruders in real-time. Computer Networks, 31, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. N. Schear, D. Albrecht, and N. Borisov. High-speed matching of vulnerability signatures. In Proc. of RAID, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. U. Shankar and V. Paxson. Active mapping: Resisting NIDS evasion without altering traffic. In Proc. of IEEE Security and Privacy, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. S. Singh, F. Baboescu, G. Varghese, and J. Wang. Packet classification using multidimensional cutting. In proc. of ACM SIGCOMM, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. R. Smith, C. Estan, and S. Jha. XFA: Faster signature matching with extended automata. In Proc. of IEEE Security and Privacy, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. R. Smith, C. Estan, S. Jha, and S. Kong. Deflating the big bang: Fast and scalable deep packet inspection with extended finite automata. In Proc. of ACM SIGCOMM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. V. Srinivasan, S. Suri, and G. Varghese. Packet classification using tuple space search. In proc. of ACM SIGCOMM, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. D. E. Taylor. Survey and taxonomy of packet classification techniques. ACM Comput. Surv., 37(3):238--275, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. H. J. Wang, C. Guo, D. R. Simon, and A. Zugenmaier. Shield: Vulnerability-driven network filters for preventing known vulnerability exploits. In Proc. of ACM SIGCOMM, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. F. Yu, Z. Chen, Y. Diao, T. V. Lakshman, and R. H. Katz. Fast and memory-efficient regular expression matching for deep packet inspection. In Proc. of ANCS, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. NetShield: massive semantics-based vulnerability signature matching for high-speed networks

    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

    Full Access

    • Published in

      cover image ACM SIGCOMM Computer Communication Review
      ACM SIGCOMM Computer Communication Review  Volume 40, Issue 4
      SIGCOMM '10
      October 2010
      481 pages
      ISSN:0146-4833
      DOI:10.1145/1851275
      Issue’s Table of Contents

      Copyright © 2010 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: 30 August 2010

      Check for updates

      Qualifiers

      • research-article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader