Detecting P2P Botnet in Software Defined Networks
Joint Authors
Su, Shang-Chiuan
Chen, Yi-Ren
Tsai, Shi-Chun
Lin, Yi-Bing
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-29
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Su, Shang-Chiuan& Chen, Yi-Ren& Tsai, Shi-Chun& Lin, Yi-Bing. 2018. Detecting P2P Botnet in Software Defined Networks. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1214162
Modern Language Association (MLA)
Su, Shang-Chiuan…[et al.]. Detecting P2P Botnet in Software Defined Networks. Security and Communication Networks No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1214162
American Medical Association (AMA)
Su, Shang-Chiuan& Chen, Yi-Ren& Tsai, Shi-Chun& Lin, Yi-Bing. Detecting P2P Botnet in Software Defined Networks. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1214162
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214162