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

Elike Komi Hodo, Xavier Bellekens, Andrew Hamilton, Pierre-Louis Dubouilh, Ephraim Tersoo Iorkyase, Christos Tachtatzis, Robert Atkinson

Research output: Contribution to conferencePaper

143 Citations (Scopus)
569 Downloads (Pure)

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.
Original languageEnglish
Number of pages6
Publication statusPublished - 14 May 2016
EventInternational Symposium on Networks, Computers and Communications - Tunisia, Hammamet, Tunisia
Duration: 11 May 201613 May 2016
Conference number: 3
http://www.isncc-conf.org/

Conference

ConferenceInternational Symposium on Networks, Computers and Communications
Abbreviated titleISNCC
CountryTunisia
CityHammamet
Period11/05/1613/05/16
Internet address

Keywords

  • security
  • neural networks
  • intrusion detection systems
  • internet of things
  • denial of service

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