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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

62 Citations (Scopus)
24 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2016 International Symposium on Networks, Computers and Communications (ISNCC)
PublisherIEEE
ISBN (Electronic)9781509002849
ISBN (Print)9781509002856
DOIs
Publication statusPublished - 17 Nov 2016
Event2016 international Symposium on Networks, Computers and Communications : 2016 ISNCC - Yasmine Hammamet, Tunisia
Duration: 11 May 201613 May 2016

Conference

Conference2016 international Symposium on Networks, Computers and Communications
CountryTunisia
CityYasmine Hammamet
Period11/05/1613/05/16

Fingerprint

Intrusion detection
Neural networks
Automobiles
Logistics
Internet
Internet of things
Denial-of-service attack

Cite this

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
Hodo, Elike ; Bellekens, Xavier ; Hamilton, Andrew ; Dubouilh, Pierre-Louis ; Iorkyase, Ephraim ; Tachtatzis, Christos ; Atkinson, Robert. / Threat analysis of IoT networks using artificial neural network intrusion detection system. 2016 International Symposium on Networks, Computers and Communications (ISNCC). IEEE , 2016.
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title = "Threat analysis of IoT networks using artificial neural network intrusion detection system",
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.",
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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 , 2016 international Symposium on Networks, Computers and Communications , Yasmine Hammamet, Tunisia, 11/05/16. https://doi.org/10.1109/ISNCC.2016.7746067

Threat analysis of IoT networks using artificial neural network intrusion detection system. / Hodo, Elike; Bellekens, Xavier; Hamilton, Andrew; Dubouilh, Pierre-Louis; Iorkyase, Ephraim; Tachtatzis, Christos; Atkinson, Robert.

2016 International Symposium on Networks, Computers and Communications (ISNCC). IEEE , 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Hodo, Elike

AU - Bellekens, Xavier

AU - Hamilton, Andrew

AU - Dubouilh, Pierre-Louis

AU - Iorkyase, Ephraim

AU - Tachtatzis, Christos

AU - Atkinson, Robert

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N2 - 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.

AB - 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.

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Hodo E, Bellekens X, Hamilton A, Dubouilh P-L, Iorkyase E, Tachtatzis C et al. Threat analysis of IoT networks using artificial neural network intrusion detection system. In 2016 International Symposium on Networks, Computers and Communications (ISNCC). IEEE . 2016 https://doi.org/10.1109/ISNCC.2016.7746067