Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (5): 838-847.DOI: 10.3778/j.issn.1673-9418.2005018

• Science Researches • Previous Articles     Next Articles

User Behavior Analysis with RNN and Graph Neural Networks

WANG Xiaodong, ZHAO Yining, XIAO Haili, WANG Xiaoning, CHI Xuebin   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Online:2021-05-01 Published:2021-04-30



  1. 1. 中国科学院 计算机网络信息中心,北京 100190
    2. 中国科学院大学,北京 100049


With the increasing amount of logs produced by nodes in CNGrid (China National Grid), traditional manual methods for user behavior analysis can no longer meet the need of daily analysis. In recent years, deep learning has shown good results in key tasks related to computer sciences, such as intrusion detection, image recognition, natural language processing and malware detection. This paper demonstrates how to apply deep learning models to user behavior analysis. To this end, this paper classifies user behavior in CNGrid and extracts a large number of user operation sequences bounded to sessions. These sequences are put into deep learning models.  This paper proposes a deep learning model that combines recurrent neural network (RNN) with graph neural network (GNN) to predict the user behavior. Graph neural network can catch the hidden state of the user’s local behavior, so it can be used as preprocessing. Recurrent neural network can catch the message of time sequence. The model is built by combining GNN and RNN to acquire both advantages. In order to verify the effectiveness of the model, this paper conducts experiments of the real user behavior datasets on CNGrid and compares them with a variety of other methods. Experimental results demonstrate the effectiveness of this novel deep learning model.

Key words: user behavior analysis, graph neural network (GNN), recurrent neural network (RNN)



关键词: 用户行为分析, 图神经网络(GNN), 循环神经网络(RNN)