Abstract
The overall performance of an intrusion protection system depends not only on the packet header classification and pattern matching, but also on the post-operative determination of correlative patterns of matched rules. An increasing number of patterns associated with a rule heighten the importance of correlative pattern matching. This work proposes a TCAM-based smart architecture that supports both deep pattern-matching and correlative pattern-matching. The proposed architecture overcomes the difficulties in implementing TCAM when the patterns are very deep and the rules for packet payload involve many patterns whose positions lie within a range. A real case payload is simulated using a Snort 2.3 rule set and simulation results demonstrate the feasibility of the proposed architecture in supporting a high-speed and robust intrusion detection and prevention system.
This work was supported by MOE Program for Promoting Academic Excellent of Universities (II) under the grant number NSC-94-2752-E-007-002-PAE, and NSC project under the grant number NSC-94-2213-E007-021.
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Wu, CC., Wen, SH., Huang, NF. (2006). Smart Architecture for High-Speed Intrusion Detection and Prevention Systems. In: Pointcheval, D., Mu, Y., Chen, K. (eds) Cryptology and Network Security. CANS 2006. Lecture Notes in Computer Science, vol 4301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11935070_22
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DOI: https://doi.org/10.1007/11935070_22
Publisher Name: Springer, Berlin, Heidelberg
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