Methodology to Improving IOT Network Security with Machine Learning Using the IOT Intrusion Dataset

Over the past 10 years, the Internet of Things (IoT) has become more significant and is currently being utilized in a number of research and development areas, such as smart cities and homes, health, industry, agriculture, security, and surveillance. IoT systems, sensors are commonly utilized as a common interface via which any devices may join a wireless sensor network and create an information system including several intelligently decision-making sensor nodes that are all operational. Furthermore, the energy depletion resulting from the restricted resources of sensor nodes is a challenging issue that reduces the lifetime of individual nodes as well as the network system overall. This paper shows how Machine Learning (ML) may be used to improve IoT network security. The IOT Intrusion Dataset was created to serve as a reference point for identifying unusual activity on IoT networks.