A rule-based hybrid method for anomaly detection in online-social-network graphs

Hassanzadeh, Reza & Nayak, Richi (2013) A rule-based hybrid method for anomaly detection in online-social-network graphs. In Bilof, Randall (Ed.) Proceedings of the 2013 IEEE 25th International Conference on Tools with Artificial Intelligence, IEEE, Hyatt Dulles, Washington DC, pp. 351-357.

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Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

Impact and interest:

1 citations in Scopus
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ID Code: 69078
Item Type: Conference Paper
Refereed: Yes
Keywords: Anomaly detection, Online social network, Fuzzy clustering
DOI: 10.1109/ICTAI.2013.60
ISBN: 9781479929719
ISSN: 1082-3409
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 by IEEE
Deposited On: 24 Mar 2014 22:34
Last Modified: 26 Mar 2014 02:00

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