A Machine Learning-based Approach for Anomaly Detection in IoT Systems

Abstract

The increased use of IoT devices has created new hurdles in the detection of anomalies. Anomaly detection is the process of discovering unexpected or abnormal behaviour in a system, and anomalies in IoT systems can be produced by a variety of sources, including hardware and software faults, cyber assaults, and environmental conditions. Machine learning-based approaches for anomaly detection in IoT systems have emerged as a viable option, harnessing the capabilities of machine learning algorithms to detect and categorise anomalies in real-time. However, there are drawbacks to these approaches, such as data quality difficulties, the necessity for real-time analysis, and the possibility of false positives and false negatives. Organizations must carefully analyse the trade-offs associated in their implementation and deployment to overcome these problems. Based on research a review of machine learning-based algorithms for anomaly detection in IoT systems. We explore the problems and potential associated with these approaches, as well as a synopsis of available datasets and models. In addition, the article describes a framework for designing and testing machine learning-based algorithms for anomaly detection in IoT systems. Overall, machine learning-based technologies have the potential to transform the way we detect and respond to abnormalities in IoT systems, but their successful implementation necessitates a cautious and deliberate approach.