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.
|Title of host publication||2016 International Symposium on Networks, Computers and Communications (ISNCC)|
|Publication status||Published - 17 Nov 2016|
|Event||2016 international Symposium on Networks, Computers and Communications : 2016 ISNCC - Yasmine Hammamet, Tunisia|
Duration: 11 May 2016 → 13 May 2016
|Conference||2016 international Symposium on Networks, Computers and Communications|
|Period||11/05/16 → 13/05/16|
Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P-L., Iorkyase, E., Tachtatzis, C., & Atkinson, R. (2016). Threat analysis of IoT networks using artificial neural network intrusion detection system. In 2016 International Symposium on Networks, Computers and Communications (ISNCC) IEEE . https://doi.org/10.1109/ISNCC.2016.7746067