Exploring Global and Local Information for Anomaly Detection with Normal
  Samples
Fan Xu Nan Wang Xibin Zhao
Anomaly detection aims to detect data that do not conform to regular
patterns, and such data is also called outliers. The anomalies to be detected
are often tiny in proportion, containing crucial information, and are suitable
for application scenes like intrusion detection, fraud detection, fault
diagnosis, e-commerce platforms, et al. However, in many realistic scenarios,
only the samples following normal behavior are observed, while we can hardly
obtain any anomaly information. To address such problem, we propose an anomaly
detection method GALDetector which is combined of global and local information
based on observed normal samples. The proposed method can be divided into a
three-stage method. Firstly, the global similar normal scores and the local
sparsity scores of unlabeled samples are computed separately. Secondly,
potential anomaly samples are separated from the unlabeled samples
corresponding to these two scores and corresponding weights are assigned to the
selected samples. Finally, a weighted anomaly detector is trained by loads of
samples, then the detector is utilized to identify else anomalies. To evaluate
the effectiveness of the proposed method, we conducted experiments on three
categories of real-world datasets from diverse domains, and experimental
results show that our method achieves better performance when compared with
other state-of-the-art methods.