Internet traffic classification by aggregating correlated naive bayes predictions

Zhang, Jun, Chen, Chao, Xiang, Yang, Zhou, Wanlei and Xiang, Yong 2013, Internet traffic classification by aggregating correlated naive bayes predictions, IEEE transactions on information forensics and security, vol. 8, no. 1, pp. 5-15.

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Title Internet traffic classification by aggregating correlated naive bayes predictions
Author(s) Zhang, Jun
Chen, Chao
Xiang, Yang
Zhou, Wanlei
Xiang, Yong
Journal name IEEE transactions on information forensics and security
Volume number 8
Issue number 1
Start page 5
End page 15
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2013
ISSN 1556-6013
1556-6021
Keyword(s) naive Bayes
network security
traffic classification
Summary This paper presents a novel traffic classification scheme to improve classification performance when few training data arc available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). We solve the BoF-based traffic classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore, a new BoF-based traffic classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated flows. We also present an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.
Language eng
Field of Research 080503 Networking and Communications
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890199 Communication Networks and Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30053662

Document type: Journal Article
Collection: School of Information Technology
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