File(s) under permanent embargo
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 XiangThis 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 securityVolume
8Issue
1Pagination
5 - 15Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
ISSN
1556-6013eISSN
1556-6021Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2013, IEEEUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC