Detecting Network Attacks in Internet of Things Devices using Back Propagation Algorithm


  • Angala Eswari S., Venkatraman K., Siva Sharma Karthick P. G.


Many of the items we use in our daily lives now have Internet connection; these devices are referred to as "smart objects." The Internet of Things (IoT) is made up of smart devices that work together to deliver complicated services. The number of IoT devices is expected to soar in the next years, with projections forecasting 20.5 billion devices in 2020 and over three trillion dollars spent on hardware alone. Verification in a dynamic environment besides a diverse variety of heterogeneous devices. Internet-of-Things (IoT) gadgets have become popular recently. IoT solutions, unlike traditional information systems, have more access to real-world contextual data and are often implemented in an environment that cannot be entirely controlled, which creates new difficulties and possibilities. In this research, we use an IoT device's awareness of its network context to give an extra security component. Our model scans a network on a regular basis and returns a list of all devices on the network. The server analyses network movements and then responds to suspicious situations. This article illustrates how our technique detects network changes that are only obtained from scanning devices on the network. Using a deep learning approach known as an Artificial Neural Network, we will uncover network faults or alterations in this project (ANN).