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Internet traffic classification by aggregating correlated naive bayes predictions

journal contribution
posted on 2013-01-01, 00:00 authored by Jun Zhang, Chao Chen, Yang Xiang, Wanlei Zhou, Yong XiangYong Xiang
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.

History

Journal

IEEE transactions on information forensics and security

Volume

8

Issue

1

Pagination

5 - 15

Publisher

IEEE

Location

Piscataway, N.J.

ISSN

1556-6013

eISSN

1556-6021

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2013, IEEE