NDLI: A multiple simple regular expression matching architecture and coprocessor for deep packet inspection

Content Provider	IEEE Xplore Digital Library
Author	Wei Zhang Yibo Xue Dongsheng Wang Tian Song
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing (Wei Zhang)
Abstract	Pattern matching and regular expression matching are all the critical components for content inspection based applications. But current regular expression matching algorithms or architecture cannot provide a perfect solution for whole matching problem. In some real network security applications, exact strings are the biggest part of rule set, and the second part is simple regular expressions (dot-star and AND-logic), and the other complex regular expressions only occupy a very small part. So, we propose a new hardware-based multiple simple regular expression matching architecture, called MSRM, for Dot-Star and AND-logic regular expressions. Firstly, software compiler splits simple regular expressions into exact strings and relations. Multi-string-matching module judges whether strings match and outputs the matched ID. Based on these matched information and pre-generated RAM data, MSRM can judge whether dot-star and AND-logic regular expressions are satisfied easily and quickly. Experiments with random test data and ClamAV rule set show that MSRM can achieve a high throughput of 2.1 and 2.8 Gbps using Virtex2 and Virtex4 devices respectively which is much higher than software algorithms.
Starting Page	1
Ending Page	8
File Size	5121060
Page Count	8
File Format	PDF
ISBN	9781424426829
DOI	10.1109/APCSAC.2008.4625475
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Random access memory Computer architecture Doped fiber amplifiers Hardware Security Pattern matching
Content Type	Text
Resource Type	Article

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