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Fast and memory-efficient regular expression matching for deep packet inspection
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Source Symposium On Architecture For Networking And Communications Systems archive
Proceedings of the 2006 ACM/IEEE symposium on Architecture for networking and communications systems table of contents
San Jose, California, USA
SESSION: Content inspection table of contents
Pages: 93 - 102  
Year of Publication: 2006
ISBN:1-59593-580-0
Authors
Fang Yu  UC Berkeley
Zhifeng Chen  Google Inc.
Yanlei Diao  University of Massachusetts, Amherst
T. V. Lakshman  Bell Laboratories, Lucent Technologies
Randy H. Katz  UC Berkeley
Sponsors
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 32,   Downloads (12 Months): 206,   Citation Count: 21
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ABSTRACT

Packet content scanning at high speed has become extremely important due to its applications in network security, network monitoring, HTTP load balancing, etc. In content scanning, the packet payload is compared against a set of patterns specified as regular expressions. In this paper, we first show that memory requirements using traditional methods are prohibitively high for many patterns used in packet scanning applications. We then propose regular expression rewrite techniques that can effectively reduce memory usage. Further, we develop a grouping scheme that can strategically compile a set of regular expressions into several engines, resulting in remarkable improvement of regular expression matching speed without much increase in memory usage. We implement a new DFA-based packet scanner using the above techniques. Our experimental results using real-world traffic and patterns show that our implementation achieves a factor of 12 to 42 performance improvement over a commonly used DFA-based scanner. Compared to the state-of-art NFA-based implementation, our DFA-based packet scanner achieves 50 to 700 times speedup.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  21

Collaborative Colleagues:
Fang Yu: colleagues
Zhifeng Chen: colleagues
Yanlei Diao: colleagues
T. V. Lakshman: colleagues
Randy H. Katz: colleagues