and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.
A multi-level perceptron, a type of supervised ANN, was trained using internet packet traces, and then, the model was assessed on its ability to thwart (DDoS/DoS) attacks REF .
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System
ArXiv
ArXiv
Computer Science