Positive and Unlabeled Learning for Anomaly Detection with Multi-features

Anomaly detection is of great interest to big data applications, and both supervised and unsupervised learning have been applied for anomaly detection. However, it still remains a challenging problem because: (1) for supervised learning, it is difficult to acquire training data for anomaly samples; while (2) for unsupervised learning, the performance may not be satisfactory due to the lack of training data. To address the limitations, we propose a hybrid solution by using both normal (positive) data and unlabeled data (could be positive or negative) for semi-supervised anomaly detection. Particularly, we introduce a new framework based on Positive and Unlabeled (PU) Learning using multi-features to detect anomalies. We extend previous PU learning methods to (1) better address unbalanced class problem which is typical for anomaly detection, and (2) handle multiple features for anomaly detection. An iterative algorithm is proposed to learn the anomaly classifier incrementally from the labeled normal data and also unlabeled data. Our proposed method is verified on three benchmark datasets and one synthetic dataset. Experimental results show that our method outperforms existing methods under different class priors and different proportions of given positive classes.