Due to the Internet of Things (IoT) device's exponential growth and the resulting expansion of their attack surfaces, hackers can launch increasingly damaging cyberattacks. The intrusion attempted to drain the target IoT network's resources through malicious activities. For IoT networks, we are utilizing machine learning methods with a carefully categorized dataset to detect intrusion. Firstly, a variety of intrusion detection datasets are presented. Second, we are utilizing the IoTID20 dataset and describing classification features. In the third step, we take some significant characteristics from the dataset. Finally, we apply machine learning methods, such as the linear algorithm, random forest, gradient boost algorithm, and many more, over the dataset to identify anomalies with high accuracy.

Flow-based intrusion detection, Internet of Things, Intrusion detection, IDS Dataset, Anomaly detection system.