  The Internet of Things (IoT) is a system that connects physical computing
devices, sensors, software, and other technologies. Data can be collected,
transferred, and exchanged with other devices over the network without
requiring human interactions. One challenge the development of IoT faces is the
existence of anomaly data in the network. Therefore, research on anomaly
detection in the IoT environment has become popular and necessary in recent
years. This survey provides an overview to understand the current progress of
the different anomaly detection algorithms and how they can be applied in the
context of the Internet of Things. In this survey, we categorize the widely
used anomaly detection machine learning and deep learning techniques in IoT
into three types: clustering-based, classification-based, and deep learning
based. For each category, we introduce some state-of-the-art anomaly detection
methods and evaluate the advantages and limitations of each technique.
