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Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System

Research paper by Elike Hodo, Xavier Bellekens, Andrew Hamilton, Pierre-louis Dubouilh, Ephraim Iorkyase, Christos Tachtatzis, Robert Atkinson

Indexed on: 07 Apr '17Published on: 07 Apr '17Published in: arXiv - Computer Science - Neural and Evolutionary Computing



Abstract

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.