Detecting P2P Botnet in Software Defined Networks

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....

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Bibliographic Details
Published inSecurity and communication networks Vol. 2018; no. 2018; pp. 1 - 13
Main Authors Lin, Yi-Bing, Tsai, Shi-Chun, Chen, Yi-Ren, Su, Shang-Chiuan
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
Hindawi Limited
Subjects
Algorithms
Communication
Communications traffic
Control equipment
Internet
Machine learning
Malware
Methods
Network security
Peer to peer computing
Security
Servers
Software
Software-defined networking
Support vector machines
Switches
Traffic flow
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Summary: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.
ISSN:1939-0114
1939-0122
DOI:10.1155/2018/4723862