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Protomatching network traffic for high throughputnetwork intrusion detection
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Source Conference on Computer and Communications Security archive
Proceedings of the 13th ACM conference on Computer and communications security table of contents
Alexandria, Virginia, USA
SESSION: Intrusion detection table of contents
Pages: 47 - 58  
Year of Publication: 2006
ISBN:1-59593-518-5
Authors
Shai Rubin  University of Wisconsin, Madison, WI
Somesh Jha  University of Wisconsin, Madison, WI
Barton P. Miller  University of Wisconsin, Madison, WI
Sponsors
SIGSAC: ACM Special Interest Group on Security, Audit, and Control
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Before performing pattern matching, a typical misuse-NIDS performs protocol analysis: it parses network traffic according to the attack protocol and normalizes the traffic into the form used by its signatures. For example, consider a NIDS that attempts to identify an HTTP-based attack. The NIDS must extract the URL from the raw traffic, convert HEX encoded characters into their equivalent ASCII form if necessary, and only then perform matching on the normalized URL. Protocol analysis is time consuming, especially in a NIDS that analyzes and normalizes all traffic just to discover that the majority of the traffic does not match any of its signatures.We develop a technique called protomatching that combines protocol analysis, normalization, and pattern matching into a single phase. The goal of the protomatching signatures is to exclude non-attack traffic quickly before the NIDS performs any further time-consuming analysis. Protomatching is based on a novel signature with two properties. First, the signature ensures that the attack pattern appears in the context that enables successful attack. This saves the need for protocol analysis. Second, the signature matches both encoded and normalized forms of an attack and this saves the need for normalization.We empirically show that a Snort implementation that uses protomatching is up to 49% faster than an unmodified Snort.


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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Collaborative Colleagues:
Shai Rubin: colleagues
Somesh Jha: colleagues
Barton P. Miller: colleagues