期刊
IEEE ACCESS
卷 7, 期 -, 页码 82624-82632出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2923814
关键词
Intrusion detection; information security; wireless network; deep belief network; support vector machine
资金
- Civil Aviation Joint Research Fund Project of the National Natural Science Foundation of China [U1833107]
In order to effectively detect wireless network intrusion behavior, a combined wireless network intrusion detection model based on deep learning was proposed. First, a feature database was generated by feature mapping, one-hot encoding, and normalization processing. Then, we built a deep belief network (DBN) with the multi-restricted Boltzmann machine (RBM) and the back propagation (BP) network. The BP network layer was connected as an auxiliary layer to the end of the RBM. The back-propagation algorithm was used to fine-tune the weight of the multi-restricted Boltzmann machine. Finally, the support vector machine (SVM) was used to train the detection method. After training, the intrusion detection model, which had the DBN-SVM detection method, was determined. The experimental results show that the detection model has good intrusion detection performance.
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