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
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.
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