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Threat analysis of IoT networks using artificial neural network intrusion detection system

Hodo, Elike Komi and Bellekens, Xavier and Hamilton, Andrew and Dubouilh, Pierre-Louis and Iorkyase, Ephraim Tersoo and Tachtatzis, Christos and Atkinson, Robert (2016) Threat analysis of IoT networks using artificial neural network intrusion detection system. In: International Symposium on Networks, Computers and Communications, 2016-05-11 - 2016-05-13, Tunisia.

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Abstract

The Internet of things (IoT) network 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 an IoT Data set, 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.