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- W2945897274 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." @default.
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- W2945897274 date "2019-05-20" @default.
- W2945897274 modified "2023-10-13" @default.
- W2945897274 title "Deep Learning SDN Intrusion Detection Scheme Based on TW-Pooling" @default.
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- W2945897274 doi "https://doi.org/10.20965/jaciii.2019.p0396" @default.
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