Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System
  The Internet of things (IoT) is still in its infancy 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.
