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Optimizing IP Flow Classification Using Feature Selection

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3 Author(s)
Dai Lei ; Chinese Acad. of Sci., Beijing ; Chen You ; Yun Xiaochun

The identification of network applications is essential to numerous network activities. Unfortunately, traditional port-based classification and packet payload-based analysis exhibit a number of shortfalls. An alternative is to use Machine Learning (ML) techniques and identify network applications based on per-flow features. Since a lot of flow features can be used for flow classification and there are many irrelevant and redundant features among them, feature selection plays a vital role in performance optimizing. In this paper, we propose a wrapper-based feature selection method for IP flow classification using modified random-mutation hill-climbing (RMHC) and C4.5 algorithm (MRMHC-C4.5). The experiments show our approach can greatly improve computational performance without negative impact on classification accuracy.

Published in:

Parallel and Distributed Computing, Applications and Technologies, 2007. PDCAT '07. Eighth International Conference on

Date of Conference:

3-6 Dec. 2007

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