Abstract
Even in its infancy, the internet of things (IoT) has enticed most of the modern industrial areas like smart cities, automobiles, medical technology. Since IoT connects everything together, it is vulnerable to a variety of devastating intrusion attacks. Being the internet of different devices makes it easy for attackers to launch their attacks. Thus, to combat all these attacks, an attack analysis is presented in this article using the basic principles of Artificial Neural Networks. Internet packet traces are used to train to the supervised ANN (Multilevel Perceptron) and evaluated after the training to decline the DDoS Attacks. This research article mainly focuses on the categorization of traffic patterns into legitimate traffic and attack traffic patterns in IoT network. The ANN processes are evaluated and tested in a simulated IoT network. The experimental results show a greater accuracy in detection of various DDoS attacks.