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Security and Communication Networks
Volume 2018 (2018), Article ID 4723862, 13 pages
https://doi.org/10.1155/2018/4723862
Research Article

Detecting P2P Botnet in Software Defined Networks

Department of Computer Science, National Chiao Tung University, Hsinchu 30050, Taiwan

Correspondence should be addressed to Shi-Chun Tsai

Received 21 January 2017; Revised 24 July 2017; Accepted 20 August 2017; Published 29 January 2018

Academic Editor: Jesús Díaz-Verdejo

Copyright © 2018 Shang-Chiuan Su et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Software Defined Network separates the control plane from network equipment and has great advantage in network management as compared with traditional approaches. With this paradigm, the security issues persist to exist and could become even worse because of the flexibility on handling the packets. In this paper we propose an effective framework by integrating SDN and machine learning to detect and categorize P2P network traffics. This work provides experimental evidence showing that our approach can automatically analyze network traffic and flexibly change flow entries in OpenFlow switches through the SDN controller. This can effectively help the network administrators manage related security problems.