Turkish Journal of Computer and Mathematics Education Vol.11 No.03 (2020), 2095-2107 DOI: https://doi.org/10.17762/turcomat.v11i3.13607 2095 Research Article A Machine Learning-based Approach for Anomaly Detection in IoT Systems Sumeshwar Singh Department of Comp. Sc. & Info. Tech., Graphic Era Hill University, Dehradun, Uttarakhand, India 248002, 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. Keywords. machine learning, anomaly detection, IoT, real-time analysis, data quality. I. Introduction The internet of things (IoT) has changed the way we interact with our surroundings. IoT gadgets have become widespread in our daily lives, ranging from smart homes to wearable devices and industrial control systems. Yet, the rapid use of IoT devices has introduced new obstacles, particularly in the detection of anomalies. The technique of detecting odd or abnormal activity in a system is known as anomaly detection. Anomalies in IoT systems can be caused by a variety of factors, including hardware and software failures, cyber assaults, and environmental conditions. It is vital to detect these anomalies in order to maintain the security and reliability of IoT systems and ensure that they continue to perform as intended. Approaches based on machine learning have emerged as a promising solution for anomaly detection in IoT systems. These technologies make use of the capabilities of machine learning algorithms to discover and classify anomalies in real time, allowing organisations to respond to and mitigate possible hazards swiftly. The ability of machine learning-based systems to assess massive volumes of complicated data from numerous sources is one of its primary advantages. IoT systems create enormous amounts of data from a wide range of sensors and devices, making it difficult for human operators to manually examine and interpret this data. Machine learning algorithms, on the other hand, can process and analyse this data on a large scale, detecting patterns and abnormalities that would otherwise go undiscovered.