  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.
