Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network

Internet of Things (IoT) is a dynamic and distributed wide network system that can integrate a gigantic number of pervasive sensors (i.e., physical objects), wireless nodes, and ubiquitous computing systems. These sensors can collect tons of raw data, send them to the internet at an unprecedented rate, and convert them to actionable insights using computing systems. These sensing nodes or physical objects are vulnerable and have upraised cybersecurity threats. In this work, we proposed the attack detection model for IoT using Software-defined network (SDN). The SDN controller can analyze the traffic flow, detect the anomaly, and block incoming traffic as well as the source nodes. In the SDN, a Fuzzy neural network (FNN) based attack detection system is considered which can detect attacks such as man-in-the-middle, distributed denial of service, side-channel, and malicious code. The FNN is trained and tested using NSL-KDD datasets. The evaluated performance exhibits that the FNN based attack detection system can detect the mentioned attack with an accuracy of 83%.