An empirical study of anomaly detection in online games

In data mining, anomaly detection aims to identify the data samples that do not conform to an expected behavior. Anomaly detection has successfully been applied to many real world applications such as fraud detection for credit cards and intrusion detection in security. However, there are very little research on using anomaly detection techniques to detect cheating in online games. In this paper, we present an empirical study of anomaly detection in online games. Four unsupervised anomaly detection techniques were used to detect abnormal players. A method for evaluating the performance these detection techniques was introduced and analysed. The experiments were conducted on one artificial dataset and two real online games at VNG company. The results show the good capability of detection techniques used in this paper in detecting abnormal players in online games. © 2016 IEEE.