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Traffic identification in big internet data
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posted on 2016-03-04, 00:00 authored by B Wang, Jun Zhang, Zili ZhangZili Zhang, Wei LuoWei Luo, D XiaThe era of big data brings new challenges to the network traffic technique that is an essential tool for network management and security. To deal with the problems of dynamic ports and encrypted payload in traditional port-based and payload-basedmethods, the state-of-the-art method employs flow statistical features and machine learning techniques to identify network traffic. This chapter reviews the statistical-feature based traffic classification methods, that have been proposed in the last decade. We also examine a new problem: unclean traffic in the training stage of machine learning due to the labeling mistake and complex composition of big Internet data. This chapter further evaluates the performance of typical machine learning algorithms with unclean training data. The review and the empirical study can provide a guide for academia and practitioners in choosing proper traffic classification methods in real-world scenarios.
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Title of book
Big data concepts, theories, and applicationsChapter number
3Pagination
129 - 156Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISBN-13
9783319277639Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2016, Springer International Publishing SwitzerlandExtent
12Editor/Contributor(s)
S Yu, S GuoUsage metrics
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