ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (4): 791-802.doi: 10.7544/issn1000-1239.2020.20190880

Special Issue: 2020数据驱动网络专题

Previous Articles     Next Articles

The Optimization Method of Wireless Network Attacks Detection Based on Semi-Supervised Learning

Wang Ting1,2, Wang Na3, Cui Yunpeng1,2, Li Huan1,2   

  1. 1(Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081);2(Key Laboratory of Big Agri-Data (Agricultural Information Institute, Chinese Academy of Agricultural Sciences), Ministry of Agriculture and Rural Areas, Beijing 100081);3(Unit 96962, Beijing 102206)
  • Online:2020-04-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61672101) and the Fundamental Research Funds of Chinese Academy of Agricultural Sciences (Y2020XC15).

Abstract: Aiming to optimize the attacks detection in high-dimensional and complex wireless network traffic data with deep learning technology, this paper proposed a WiFi-ADOM (WiFi network attacks detection optimization method) based on semi-supervised learning. Firstly, based on stacked sparse auto-encoder (SSAE), which is an unsupervised learning model, two types of network traffic feature representation vectors are proposed: new feature value vector and original feature weight value vector. Then, the original feature weight value vector is used to initialize the weight value of the supervised learning model deep neural network to obtain the preliminary result of the attack type, and the unsupervised learning clustering method Bi-kmeans is used to produce the corrective term for unknown attacks discrimination with the new feature value vectors. Finally, the preliminary result of the attack type and the corrective term of the unknown attacks discrimination are combined to obtain the final result of the attack type. Compared with the existing attacks detection methods with the public wireless network traffic data set AWID, the optimal performance of the method of WiFi-ADOM for network attacks detection is verified. At the same time, the importance of features in network attacks detection is explored. The results show that the method of WiFi-ADOM can effectively detect unknown attacks while ensuring detection performance.

Key words: network attacks detection, network intrusion detection, semi-supervised learning, deep learning, Bi-kmeans clustering

CLC Number: