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Anomaly detection aims to detect data that do not conform to regularpatterns, and such data is also called outliers. The anomalies to be detectedare often tiny in proportion, containing crucial information, and are suitablefor application scenes like intrusion detection, fraud detection, faultdiagnosis, e-commerce platforms, et al. However, in many realistic scenarios,only the samples following normal behavior are observed, while we can hardlyobtain any anomaly information. To address such problem, we propose an anomalydetection method GALDetector which is combined of global and local informationbased on observed normal samples. The proposed method can be divided into athree-stage method. Firstly, the global similar normal scores and the localsparsity scores of unlabeled samples are computed separately. Secondly,potential anomaly samples are separated from the unlabeled samplescorresponding to these two scores and corresponding weights are assigned to theselected samples. Finally, a weighted anomaly detector is trained by loads ofsamples, then the detector is utilized to identify else anomalies. To evaluatethe effectiveness of the proposed method, we conducted experiments on threecategories of real-world datasets from diverse domains, and experimentalresults show that our method achieves better performance when compared withother state-of-the-art methods.

Exploring Global and Local Information for Anomaly Detection with Normal Samples