Abstract

There are various aspects to Internet of Things security, such as guaranteeing the safety of both the devices and the Internet of Things networks to which they connect. Many other types of equipment, including industrial robots, smart grids, construction automation systems, entertainment gadgets, and many more, are included in this, despite the fact that they were not designed with network security in mind. When it comes to securing systems, networks, and data, IoT device security must be able to resist a wide range of IoT security assaults. One of the most important issues in the field of data security is the creation of intrusion detection systems (IDSs) for the Internet of Things. Client devices (edge devices) in Federated Learning utilize local data to train the machine learning model, and then send the updated model parameters to a cloud server so that they may be aggregated (rather than raw data).This paper proposes a machine learning system that employs federated learning to detect intrusions in the IoT. The FedAVG algorithm is employed to aggregate models. Models are trained locally by nodes. The models are trained and validated using machine learning methods, including Random Forest, ID3, and Support Vector Machine. The NSL KDD data set is employed to undertake experiments.