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

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 conference › Paper › peer-review

402 Citations (Scopus)

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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 language	English
Number of pages	6
Publication status	Published - 14 May 2016
Event	International Symposium on Networks, Computers and Communications - Tunisia, Hammamet, Tunisia Duration: 11 May 2016 → 13 May 2016 Conference number: 3 http://www.isncc-conf.org/

Conference

Conference	International Symposium on Networks, Computers and Communications
Abbreviated title	ISNCC
Country/Territory	Tunisia
City	Hammamet
Period	11/05/16 → 13/05/16
Internet address	http://www.isncc-conf.org/

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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AU - Hodo, Elike Komi

AU - Bellekens, Xavier

AU - Hamilton, Andrew

AU - Dubouilh, Pierre-Louis

AU - Iorkyase, Ephraim Tersoo

AU - Tachtatzis, Christos

AU - Atkinson, Robert

N1 - Conference code: 3

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N2 - 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.

AB - 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.

KW - security

KW - neural networks

KW - intrusion detection systems

KW - internet of things

KW - denial of service

UR - http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=48300

M3 - Paper

T2 - International Symposium on Networks, Computers and Communications

Y2 - 11 May 2016 through 13 May 2016

ER -

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

Abstract

Conference

Keywords

UN SDGs

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