JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2018, Vol. 53 ›› Issue (9): 1-11.doi: 10.6040/j.issn.1671-9352.2.2017.169

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Anomaly detection model of host group based on graph-evolution events

YE Xiao-ming1, CHEN Xing-shu2*, YANG Li3, WANG Wen-xian2, ZHU Yi1, SHAO Guo-lin1, LIANG Gang2   

  1. 1. College of Computer Science, Sichuan University, Chengdu 610065, Sichuan, China;
    2. College of Cybersecurity, Sichuan University, Chengdu 610065, Sichuan, China;
    3. School of Economics and Management, Southwest Jiao Tong University, Chengdu 610031, Sichuan, China
  • Received:2017-08-28 Online:2018-09-20 Published:2018-09-10

Abstract: Aiming at the communication behavior based on service aggregation and the new collaborative attack mode that is typical of distributed attack in the network environment, the anomaly detection model of host group based on graph-evolution events is proposed. It analyzes the potential socialization of actors, the clustering of host clusters and the dynamics of their group behavior. The model has the characteristics of no parameters and extensible data magnitude. The dynamic evolution events and detection algorithms are defined and proposed to detect abnormal host groups. The model is implemented and deployed on Spark, and the data from the actual computer and network environment is analyzed and verified. The experimental results show that this model can effectively describe group behavior, expose important graph-evolution events, and locate the host group with abnormal occurrence accurately. The detection rate of group members is 95.09%.

Key words: graph-evolution event, host group, group behavior, anomaly detection

CLC Number: 

  • TN915.08
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