Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment
It addresses the challenge of anomaly data in IoT networks, which is crucial for security and reliability, but is incremental as it synthesizes existing research.
This survey tackles the problem of anomaly detection in IoT environments by categorizing and evaluating current machine learning and deep learning techniques, including clustering-based, classification-based, and deep learning methods, without presenting new experimental results.
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.