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JACIII Vol.23 No.3 pp. 396-401
doi: 10.20965/jaciii.2019.p0396
(2019)

Paper:

Deep Learning SDN Intrusion Detection Scheme Based on TW-Pooling

Qingyue Meng*, Shihui Zheng*, and Yongmei Cai**

*School of Cyberspace Security, Beijing University of Posts and Telecommunications
West TuCheng Road 10, Haidian, Beijing 100876, China

**School of Computer Science and Engineering, Xinjiang University of Finance and Economics
No.449 Beijing Middle Road, Urumqi, Xinjiang Uygur Autonomous Region 830026, China

Received:
June 6, 2018
Accepted:
July 24, 2018
Published:
May 20, 2019
Keywords:
Software Defined Network, control plane, attack detection
Abstract

The numerical control separation in the Software-Defined Network (SDN) allows the control plane to have the absolute management rights of the network. As a new management plane of the SDN, once it is attacked, it will cause the entire network to face flaws. For this reason, this paper proposes a SDN control plane attack detection scheme based on deep learning, which can detect and respond to attacks on the SDN control plane in time. In this scenario, we propose a new pooling scheme that uses the TF-IDF idea to weight the characteristics of network traffic. Ultimately, our method achieved an accuracy of 99.8% in the SDN network’s traffic data set including 24 attack types.

Cite this article as:
Q. Meng, S. Zheng, and Y. Cai, “Deep Learning SDN Intrusion Detection Scheme Based on TW-Pooling,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.3, pp. 396-401, 2019.
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