Considering diversified HTTP types, the performance bottleneck of signature-based classification must be resolved. We define a signature model classifying the traffic in multiple dimensions and suggest a hierarchical signature structure to remove signature redundancy and minimize search space. Our experiments on campus traffic demonstrated 1.8 times faster processing speed than the Aho-Corasick matching algorithm in Snort.

Internet traffic classification is an essential step for stable service provision. The payload signature classifier is considered a reliable method for Internet traffic classification but is prohibitively computationally expensive for real-time handling of large amounts of traffic on high-speed networks. In this paper, we describe several design techniques to minimize the search space of traffic classification and improve the processing speed of the payload signature classifier. Our suggestions are (1) selective matching algorithms based on signature type, (2) signature reorganization using hierarchical structure and traffic locality, and (3) early packet sampling in flow. Each can be applied individually, or in any combination in sequence. The feasibility of our selections is proved via experimental evaluation on traffic traces of our campus and a commercial ISP. We observe 2 to 5 times improvement in processing speed against the untuned classification system and Snort Engine, while maintaining the same level of accuracy.