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.
@article{arxiv.1704.02286,
title = {Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System},
author = {Elike Hodo and Xavier Bellekens and Andrew Hamilton and Pierre-louis Dubouilh and Ephraim Iorkyase and Christos Tachtatzis and Robert Atkinson},
journal= {arXiv preprint arXiv:1704.02286},
year = {2017}
}
Comments
Published in The 2016 International Symposium on Networks, Computers and Communications (IEEE ISNCC'16) , Hammamet, Tunisia, 2016