Multiattack Intrusion Detection Algorithm for Edge-Assisted Internet of Things

With the continuous development of edge-assisted internet of things (IoT) applications, a large number of computing tasks need to be offloaded into the edge servers. It is critical to secure the security of IoT data transmission to the edge. In this paper, we propose a multi-attack intrusion detection system (IDS) for edge-assisted IoT using the back propagation (BP) Neural Network, and Radial basis function (RBF) neural network. Especially, BP neural network is exploited to find the abnormal and get the importance of features for each attack. Radial basis function (RBF) neural network is used for detecting multi-attack intrusion. The results show high accuracy in proposed multi-attack scenario and elucidate the feasibility and preeminence of our proposed anomaly detection framework.