NDLI: MINETRAC: Mining flows for unsupervised analysis & semi-supervised classification

Content Provider	IEEE Xplore Digital Library
Author	Casas, P. Mazel, J. Owezarski, P.
Copyright Year	2011
Description	Author affiliation: CNRS; LAAS; 7 avenue du colonel Roche, F-31077 Toulouse Cedex 4, France (Casas, P.; Mazel, J.; Owezarski, P.)
Abstract	Driven by the well-known limitations of port-based and payload-based analysis techniques, the use of Machine Learning for Internet traffic analysis and classification has become a fertile research area during the past half-decade. In this paper we introduce MINETRAC, a combination of unsupervised and semi-supervised machine learning techniques capable of identifying and classifying different classes of IP flows sharing similar characteristics. The unsupervised analysis is accomplished by means of robust clustering techniques, using Sub-Space Clustering, Evidence Accumulation, and Hierarchical Clustering algorithms to explore inter-flows structure. MINETRAC permits to identify natural groupings of traffic flows, combining the evidence of data structure provided by different partitions of the same set of traffic flows. Automatic classification is performed by means of semi-supervised learning, using only a small fraction of ground-truth flows to map the identified clusters into their associated most-probable originating network service or application. We evaluate the performance of MINETRAC using real traffic traces, additionally comparing its performance against previously proposed clustering-based flow analysis methods and supervised/semi-supervised classification approaches.
Starting Page	87
Ending Page	94
File Size	296220
Page Count	8
File Format	PDF
ISBN	9781457711879
e-ISBN	9780983628309
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	ITC
Subject Keyword	Training Algorithm design and analysis Accuracy Computational modeling Sub-Space Clustering Evidence Accumulation Clustering algorithms Unsupervised Traffic Analysis Vegetation Semi-Supervised Traffic Classification Hierarchical Clustering Partitioning algorithms
Content Type	Text
Resource Type	Article

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