Dynamic Group Behavior Analysis and Its Application in Network Abnormal Behavior Detection

  • Yan Tong
  • Jian Zhang
  • Wei Chen
  • Mingdi Xu
  • Tao Qin
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 237)


Focus on the difficulty of large-scale network traffic monitoring and analysis, this paper proposed the concepts of Group Behavior Flow model to aggregate traffic packets and perform abnormal behavior detection. Based on the flow model the pivotal traffic metrics can be extracted while the number of flow records are reduced significantly. Secondly, we employ the graph model to capture the traffic feature distribution between different group users. And optical flow analysis methods are proposed to extract the dynamic behavior changing features between different groups and achieve the goal of abnormal behavior detection. The experimental results based on actual traffic traces show that the methods proposed in this paper can capture the traffic features effectually in the current 10 Gbps network environment, and achieve the goal of abnormal behavior detection and abnormal source location, which is very important for traffic management.


Group user model Dynamic behavior Optical flow analysis Abnormal detection 



The research presented in this paper is supported in part by the Natural Science Foundation of China (61502438, 61672026), Natural Science Foundation of Shaanxi Province (2016JM6040), and Chinese Defense Advance Research Program (B0820132036).


  1. 1.
    Baldi, M., Baralis, E., Risso, F.: Data mining techniques for effective and scalable traffic analysis. In: IEEE International Symposium on Integrated Network Management, 15–19 May 2005, pp. 105–118 (2005)Google Scholar
  2. 2.
    Zhou, A., Guang, C., Guo, X.: High-speed network traffic measurement methods. J. Softw. 25(1), 135–153 (2014)Google Scholar
  3. 3.
    Zhang, B., Yang, J., Wu, J.: Survey and analysis on the internet traffic model. J. Softw. 22(01), 115–131 (2011)CrossRefGoogle Scholar
  4. 4.
    Wang, J., Rossell, D., Cassandras, C.G., et al.: Network anomaly detection: a survey and comparative analysis of stochastic and deterministic methods. In: Proceedings of the 52nd IEEE Conference on Decision and Control, Florence, Italy (2013)Google Scholar
  5. 5.
  6. 6.
    Kim, S.S., Reddy, A.L.N.: A study of analyzing network traffic as images in real-time. In: 24th Annual Joint Conference of the IEEE Computer and Communications Societies, 13–17 March 2005, vol. 3, pp. 2056–2067 (2005)Google Scholar
  7. 7.
    Lakhina, A., Papagiannaki, K., Crovella, M., et al.: Structural analysis of network traffic flows. In: Proceedings of the Joint International Conference on Measurement and Modeling Of Computer Systems, pp. 61–72 (2004)Google Scholar
  8. 8.
    Freedman, M.J., Vutukuru, M., Feamster, N., et al.: Geographic locality of IP prefixes. In: Proceedings of the ACM Internet Measurement Conference Berkeley, CA, pp. 153–158, October 2005Google Scholar
  9. 9.
    Wang, X., Zhang, G.: Research on moving object detection method based on optical flow. J. Comput. Eng. Appl. 40(1), 43–46 (2004)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Yan Tong
    • 1
  • Jian Zhang
    • 1
  • Wei Chen
    • 1
  • Mingdi Xu
    • 2
  • Tao Qin
    • 3
  1. 1.System Research DepartmentWuhan Digital Engineering InstituteWuhanChina
  2. 2.System Software DepartmentWuhan Digital Engineering InstituteWuhanChina
  3. 3.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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