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An unsupervised ensemble framework for node anomaly behavior detection in social network

  • Qing Cheng
  • Yun ZhouEmail author
  • Yanghe Feng
  • Zhong Liu
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Abstract

Large-scale and dynamic networks arise in cyberspace and financial security. Given a dynamic network, it is crucial to detect structural anomalies, such as node behaviors deviate from underlying majority of the network. However, anomaly analysis for dynamic networks is difficult to precisely detect the anomalous behaviors of nodes because it usually ignores the evolutionary behaviors of different nodes. Our work taps into this gap and proposes an unsupervised ensemble framework for node temporal behavior modeling and node behavior real-time anomaly detection. Specifically, a latent space model is used to model the node behavior; each node is assigned a probability distribution across a small set of roles based on that node’s features. The evolutionary behavior of node is represented as node roles change over time and the anomalies of node are identified as deviations from expected roles. The entropy-based ensembles method is proposed to combine with multiple unsupervised anomaly detectors to yield robust performances, which achieves the real-time anomaly detection for different types of node behaviors. Finally, we show the effectiveness of the proposed method on Enron network in the experiments.

Keywords

Network node modeling Anomaly behavior detection Entropy-based ensembles 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61703416) and Natural Science Foundation of Hunan Province, China (Grant No. 2018JJ3614).

Compliance with ethical standards

Conflict of Interest

Qing Cheng, Yanghe Feng and Zhong Liu declare that they have no conflict of interest. Yun Zhou has received research grants from NSFC and NSF-Hunan.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Systems EngineeringNational University of Defense TechnologyChangshaChina
  2. 2.Science and Technology on Information Systems Engineering LaboratoryNational University of Defense TechnologyChangshaChina

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