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Multi-Constrained Multi-Path Routing for Server-Centric Data Center Networks Read chapter Read first chapter

2015 | OriginalPaper | Chapter

P2P Traffic Identification Using Support Vector Machine and Cuckoo Search Algorithm Combined with Particle Swarm Optimization Algorithm

Authors : Zhiwei Ye, Mingwei Wang, Chunzhi Wang, Hui Xu

Published in: Frontiers in Internet Technologies

Publisher: Springer Berlin Heidelberg

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Abstract

As peer-to-peer (P2P) technology booms lots of problems arise such as rampant piracy, congestion, low quality etc. Thus, accurate identification of P2P traffic makes great sense for efficient network management. As one of the optimal classifiers, support vector machine (SVM) has been successfully used in P2P traffic identification. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. In the paper, a novel hybrid method to optimize parameters of SVM based on cuckoo search algorithm combined with particle swarm optimization algorithm is proposed. The first stage of the proposed approach is to tune the best parameters for SVM with training data. Subsequently, the SVM configured with the best parameters is employed to identify P2P traffic. In the end, we demonstrate the effectiveness of our approach on-campus traffic traces. Experimental results indicate that the proposed method outperforms SVM based on genetic algorithm, particle swarm optimization algorithm and cuckoo search algorithm.

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Metadata
Title
P2P Traffic Identification Using Support Vector Machine and Cuckoo Search Algorithm Combined with Particle Swarm Optimization Algorithm
Authors
Zhiwei Ye
Mingwei Wang
Chunzhi Wang
Hui Xu
Copyright Year
2015
Publisher
Springer Berlin Heidelberg
Book
Frontiers in Internet Technologies

Print ISBN: 978-3-662-46825-8

Electronic ISBN: 978-3-662-46826-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-662-46826-5_10

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