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Journal of Network and Systems Management

Erschienen in:

01.01.2015

Automated Dataset Generation for Training Peer-to-Peer Machine Learning Classifiers

verfasst von: Roozbeh Zarei, Alireza Monemi, Muhammad Nadzir Marsono

Erschienen in: Journal of Network and Systems Management | Ausgabe 1/2015

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Abstract

Peer-to-peer (P2P) classifications based on flow statistics have been proven accurate in detecting P2P traffic. A machine learning classification is affected by the quality and recency of the training dataset used. Hence, to classify P2P traffic on-line requires the removal of these limitations. In this paper, an automated training dataset generation for an on-line P2P traffic classification is proposed to allow frequent classifier retraining. A two-stage training dataset generator (TSTDG) is proposed by combining a 3-class heuristic and a 3-class statistical classification to automatically generate a training dataset. In the heuristic stage, traffic is classified as P2P, non-P2P, or unknown. In the statistical stage, a dual Decision Tree is built based on a dataset generated in the heuristic stage to reduce the amount of classified unknown traffic. The final training dataset is generated based on all flows that are classified in these two stages. The proposed system has been evaluated on traces captured from a campus network. The overall results show that the TSTDG can generate an accurate training dataset by classifying around 94 % of total flows with high accuracy (98.59 %) and a low false positive rate (1.27 %).

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Flows are distinguished based on [Source IP, Destination IP, Src Port, Dst Post, Protocol].

J48 is an open source C++ implementation of the C4.5 algorithm

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Titel: Automated Dataset Generation for Training Peer-to-Peer Machine Learning Classifiers
verfasst von: Roozbeh Zarei
Alireza Monemi
Muhammad Nadzir Marsono
Publikationsdatum: 01.01.2015
Verlag: Springer US
Erschienen in: Journal of Network and Systems Management / Ausgabe 1/2015
Print ISSN: 1064-7570
Elektronische ISSN: 1573-7705
DOI: https://doi.org/10.1007/s10922-013-9279-z

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Acknowledgment to Reviewers