Outlier Detection Approaches Based on Machine Learning in the Internet-of-Things

Outlier detection in the Internet of Things (IoT) is an essential challenge issue studied in numerous fields, including fraud monitoring, intrusion detection, secure localization, trust management, and so on. Conventional outlier detection technologies cannot be used directly in IoT due to the open nature of wireless communication as well as the resource-constrained characteristics of end nodes. Therefore, this article provides a comprehensive survey of new outlier detection approaches based on machine learning for IoT. The approaches are first carefully discussed based on their adopted machine learning algorithms. In addition, the performance of them with respect to the advantages and the drawbacks are compared in detail, which naturally leads to some open research issues that are analyzed afterward.