The growth of IoT networks has brought new security risks, especially in detecting unusual activities that may indicate attacks or failures. Traditional security methods struggle with the dynamic nature of IoT environments. This paper explores the use of Machine Learning (ML) techniques, such as Support Vector Machines (SVM), Decision Trees, and Clustering, for detecting anomalies in IoT networks. The focus is on developing automated and scalable systems to improve IoT security and reliability, along with suggestions for overcoming existing challenges

IoT, Anomaly Detection, Supervised learning, Unsupervised learning, SVM, Decision Trees

This research explored how machine learning can be used to detect unusual activities in IoT networks. We found that while machine learning works well for finding known attacks, it has some challenges when dealing with large, changing IoT networks. These challenges include handling lots of data in real-time, needing labeled data, and managing the different types of data from various IoT devices. Also, many models need too much computing power, which is a problem for devices with limited resources. However, machine learning still has great potential for improving security in IoT networks. To overcome these problems, future work should focus on methods that don't need a lot of labeled data, like unsupervised learning. Combining different techniques and using lighter, more efficient models can also help make anomaly detection faster and more reliable. Additionally, using edge computing can improve detection speed without putting too much strain on IoT devices. In conclusion, while there are challenges, machine learning can greatly improve the security of IoT networks. With more research, we can create better systems to protect IoT networks from different threats.

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