“…Based on the anomaly types identified in the previous stage, the second stage trains a Bayesian maximum likelihood classifier to identify the anomalies. Recently, researchers investigated data-driven methods for gas theft detection [2], [3], [6], [7], which are closely related to this study. In [2], Yang et al proposed a method based on normal user modeling and RankNet for detecting gas theft suspects among restaurant users.…”