Proceedings

One-Class Conditional Random Fields for Sequential Anomaly Detection

Yale Song, Zhen Wen, Ching-Yung Lin, Randall Davis

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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1685-1690.

Abstract

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.

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. IJCAI, 1685–1690. https://ijcai.org/proceedings/2013/250
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