LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
ACM Computing Surveys (CSUR)
Mining (Social) Network Graphs to Detect Random Link Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Spectrum based fraud detection in social networks
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
OddBall: spotting anomalies in weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Analyzing the effectiveness of graph metrics for anomaly detection in online social networks
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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In this paper, we propose a semi-supervised approach of anomaly detection in Online Social Networks. The social network is modeled as a graph and its features are extracted to detect anomaly. A clustering algorithm is then used to group users based on these features and fuzzy logic is applied to assign degree of anomalous behavior to the users of these clusters. Empirical analysis shows effectiveness of this method.