One-Class Conditional Random Fields for Sequential Anomaly Detection
Author(s)Song, Yale; Wen, Zhen; Lin, Ching-Yung; Davis, Randall
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Sequential anomaly detection is a challenging problem due to the one-class nature of the data (i.e., data is collected from only one class) and the temporal dependence in sequential data. We present One-Class Conditional Random Fields (OCCRF) for sequential anomaly detection that learn from a one-class dataset and capture the temporal dependence structure, in an unsupervised fashion. We propose a hinge loss in a regularized risk minimization framework that maximizes the margin between each sequence being classified as "normal" and "abnormal." This allows our model to accept most (but not all) of the training data as normal, yet keeps the solution space tight. Experimental results on a number of real-world datasets show our model outperforming several baselines. We also report an exploratory study on detecting abnormal organizational behavior in enterprise social networks.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Association for Computing Machinery (ACM)
Yale Song, Zhen Wen, Ching-Yung Lin, and Randall Davis. 2013. One-class conditional random fields for sequential anomaly detection. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence (IJCAI'13), Francesca Rossi (Ed.). AAAI Press 1685-1691.
Author's final manuscript