Abstract
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.
Highlights
With the development of wireless network and computer technology, wireless network intrusion tends to be frequent
Due to the insufficient detection of wireless network intrusion efficiency and accuracy in the above research, a combined wireless network intrusion detection model based on deep learning was proposed
DEEP BELIEF NETWORK deep belief network (DBN) is a generation model with numerous hidden variable layers that composed of multi-layer unsupervised learning restricted Boltzmann machine (RBM) and a supervised back propagation neural network layer
Summary
With the development of wireless network and computer technology, wireless network intrusion tends to be frequent. Using machine learning methods to improve the effectiveness and accuracy rate of intrusion detection has become a research hotspot in the field of wireless network security. Sun et al [19] A method of improving Bayesian network intrusion detection based on deep learning and sliding window was proposed to solve the problem of attribute redundancy in training dataset. Gao et al [20] proposed a multi-class support vector machine intrusion detection (DBN-MSVM) method based on the deep belief network. H. Yang et al.: Combined Wireless Network Intrusion Detection Model Based on Deep Learning was proposed, which can effectively solve the classification problem of massive intrusion data. Due to the insufficient detection of wireless network intrusion efficiency and accuracy in the above research, a combined wireless network intrusion detection model based on deep learning was proposed. The data set was added a timestamp by Python’s time module
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