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

doi:10.1109/ISNCC.2016.7746067

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 proceeding › Conference contribution

92 Citations (Scopus)

39 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 language	English
Title of host publication	2016 International Symposium on Networks, Computers and Communications (ISNCC)
Publisher	IEEE
ISBN (Electronic)	9781509002849
ISBN (Print)	9781509002856
DOIs	https://doi.org/10.1109/ISNCC.2016.7746067
Publication status	Published - 17 Nov 2016
Event	2016 international Symposium on Networks, Computers and Communications : 2016 ISNCC - Yasmine Hammamet, Tunisia Duration: 11 May 2016 → 13 May 2016

Conference

Conference	2016 international Symposium on Networks, Computers and Communications
Country	Tunisia
City	Yasmine Hammamet
Period	11/05/16 → 13/05/16

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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

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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.",

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

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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 proceeding › Conference contribution

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T1 - Threat analysis of IoT networks using artificial neural network intrusion detection system

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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DO - 10.1109/ISNCC.2016.7746067

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Access to Document

10.1109/ISNCC.2016.7746067

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