NDLI: Identification and Selection of Flow Features for Accurate Traffic Classification in SDN

Content Provider	IEEE Xplore Digital Library
Author	Santos Da Silva, A. Cleder Machado, C. Vebber Bisol, R. Zambenedetti Granville, L. Schaeffer-Filho, A.
Copyright Year	2015
Description	Author affiliation: Alberto Schaeffer-Filho Inst. of Inf., Fed. Univ. of Rio Grande do Sul, Porto Alegre, Brazil (Santos Da Silva, A.; Cleder Machado, C.; Vebber Bisol, R.; Zambenedetti Granville, L.; Schaeffer-Filho, A.)
Abstract	Software-Defined Networking (SDN) aims to alleviate the limitations imposed by traditional IP networks by decoupling network tasks performed on each device in particular planes. This approach offers several benefits, such as standard communication protocols, centralized network functions, and specific network elements, for example, controller devices. Despite these benefits, there is still a lack of adequate support for performing tasks related to traffic classification, because (i) there are traffic profiles that are very similar, which makes their classification difficult (e.g., Both HTTP and DNS flows are characterized by packet bursts), (ii) Open Flow, the key SDN implementation today, only offers native flow features, such as packet and byte count, that do not describe intrinsic traffic profiles, and (iii) there is a lack of support to determine what is the optimal set of flow features to characterize different types of traffic profiles. In this paper, we introduce an architecture to collect, extend, and select flow features for traffic classification in Open Flow-based networks. The main goal of our solution is to offer an extensive set of flow features that can be analyzed and refined and to be capable of finding the optimal subset of features to classify different types of traffic flows. The experimental evaluation of our proposal shows that some features emerge as meaningful, occupying the top positions for the classification of distinct flows in different experimental scenarios.
Starting Page	134
Ending Page	141
File Size	586258
Page Count	8
File Format	PDF
e-ISBN	9781509018499
DOI	10.1109/NCA.2015.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radiation detectors Principal component analysis IP networks Computer architecture Monitoring Genetic algorithms Algorithm design and analysis Software-Defined Networking Flow Feature Feature selection Traffic Classification
Content Type	Text
Resource Type	Article

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