Combined Wireless Network Intrusion Detection Model Based on Deep Learning

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.