Anomaly Detection in Dynamic Social Networks Based on Behavioral Measurements

Document Type : Original Article

Authors

1 Computer Engineering Department, Bu-Ali SIna University

2 Computer Engineering Department, Engineering Faculty,, Bu-Ali SIna University

Abstract

Since the detection of anomalies in dynamic social networks takes place in a sequence of graphs over time, in addition to the storage management challenge, the detection process is difficult due to the slow evolution of graphs. A number of graphs are selected in the specified time frame, and by examining the changes of these graphs, the possible anomalies are detected. Therefore, choosing the number of time points (graphs) in the sequence of graphs is an important challenge in the detection of anomalies. In this paper, a novel method is proposed to detect anomalies based on structural data extracted from dynamic social network graphs. By extracting the centrality indicators from the network graph and their normalized mean, the    activity criterion for each individual has been defined. Over time, changes in the activity criterion for each individual are measured and marked as the possibility of normal or abnormal behavior. If the individual's behavior measure exceeds a certain threshold, it is reported as an anomaly. The results show that the     proposed method detects more anomalies with the accuracy and recall of 64.29 and 81.82 respectively, for the VAST 2008 data set. It also, detects more anomalies by selecting different number of time points in the graph sequence.
 

Keywords

References

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Electronic and Cyber Defense
Volume 9, Issue 1 - Serial Number 33
Serial No. 33, Spring Quarterly
April 2021
Pages 115-123

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History

  • Receive Date: 28 April 2020
  • Revise Date: 12 November 2020
  • Accept Date: 14 November 2020
  • Publish Date: 21 April 2021

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