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Highly Space Efficient Counters for Perl Compatible Regular Expressions in FPGAs

  • Chia-Tien Dan Lo
  • Yi-Gang Tai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4943)

Abstract

Signature based network intrusion detection systems (NIDS) rely on an underlying string matching engine that inspects each network packet against a known malicious pattern database. Traditional static pattern descriptions may not efficiently represent sophisticated attack signatures. Recently, most NIDSs have adopted regular expressions such as Perl compatible regular expressions (PCREs) to describe an attack signature, especially for polymorphic worms. PCRE is a superset of traditional regular expression, in which no counters are involved. However, this overloads the performance of software-based NIDSs, causing a big portion of their execution time to be dedicated to pattern matching. Over the past decade, hardware acceleration for the pattern matching has been studied extensively and a marginal performance has been achieved. Among hardware approaches, FPGA-based acceleration engines provide great flexibility because new signatures can be compiled and programmed into their reconfigurable architecture. As more and more malicious signatures are discovered, it becomes harder to map a complete set of malicious signatures specified in PCREs to an FPGA chip. Even worse is that the counters used in PCREs typically take a great deal of hardware resources. Therefore, we propose a space efficient SelectRAM counter for PCREs that involve counting. The design takes advantage of components that consist of a configurable logic block, and thus optimizes space usage. A set of PCRE blocks has been built in hardware to implement PCREs used in Snort/Bro. Experimental results show that the proposed sheme outperforms existing designs by at least 5-fold. Performance results are reported in this paper.

Keywords

Intrusion Detection Regular Expression Hardware Resource Half Adder Network Intrusion Detection System 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Chia-Tien Dan Lo
    • 1
  • Yi-Gang Tai
    • 1
  1. 1.Department of Computer ScienceUniversity of Texas at San Antonio 

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