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Robust traffic classification with mislabelled training samples
conference contribution
posted on 2015-01-01, 00:00 authored by B Wang, Jun Zhang, Zili ZhangZili Zhang, Wei LuoWei Luo, D XiaTraffic classification plays the significant role in the network security and management. However, accurate classification is challenging if the training data is contaminated with unclean traffic. Recent researches often assume clean training data, and hence performance reduced on real-time network traffic. To meet this challenge, in this paper, we propose a robust method, Unclean Traffic Classification (UTC), which incorporates noise elimination and suspected noise reweighting. Firstly, UTC eliminates strong noisy training data identified by a consensus filtering with multiple classifiers. Furthermore, UTC estimates the relevance of remaining training data and learns a robust traffic classifier. Through a number of experiments on a real-world traffic dataset, we show that the new method outperforms existing state-of-the-art traffic classification methods, under the extremely difficult circumstance with unclean training data.
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Event
IEEE Parallel and Distributed Systems. International Conference (21st : 2015 : Melbourne, Vic.)Pagination
328 - 335Publisher
IEEELocation
Melbourne, Vic.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2015-12-14End date
2015-12-17ISSN
1521-9097ISBN-13
9780769557854Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ICPADS 2015: Proceedings of the IEEE Parallel and Distributed Systems 2015 International ConferenceUsage metrics
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