Anomaly Detection Techniques using Deep Learning in IoT: A Survey

IoT technologies is improving life quality by enhancing several real-life smart applications. IoT includes large number of devices generating huge amount of data which needs large computation. Anomaly detection and security is the major concern in the IoT domain. This survey paper provides an overview of anomaly detection using machine learning and deep learning methods in IoT applications. Machine learning and deep learning are powerful tools for analyzing normal and abnormal behavior of IoT components and devices. In this paper we outline key issues in research and challenges using deep anomaly detection techniques for resource constrained devices in real-world problems of IoT. Fog computing move the computation at the device or the edge to solve some of the issues of network security and delay in computation.