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

309 Citations (Scopus)

169 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
Externally published	Yes
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/Territory	Tunisia
City	Yasmine Hammamet
Period	11/05/16 → 13/05/16

Keywords

Internet of things
Denial of service
Intrusion detection system
Multi-level perceptron
Artificial Neural Network

UN SDGs

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

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10.1109/ISNCC.2016.7746067

Bellekens_ThreatAnalysisOfIoT_Author_2016Final published version, 750 KB

Cite this

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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",

year = "2016",

month = nov,

day = "17",

doi = "10.1109/ISNCC.2016.7746067",

language = "English",

isbn = "9781509002856",

booktitle = "2016 International Symposium on Networks, Computers and Communications (ISNCC)",

publisher = "IEEE ",

note = "2016 international Symposium on Networks, Computers and Communications : 2016 ISNCC ; Conference date: 11-05-2016 Through 13-05-2016",

}

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

TY - GEN

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

PY - 2016/11/17

Y1 - 2016/11/17

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.

U2 - 10.1109/ISNCC.2016.7746067

DO - 10.1109/ISNCC.2016.7746067

M3 - Conference contribution

SN - 9781509002856

BT - 2016 International Symposium on Networks, Computers and Communications (ISNCC)

PB - IEEE

T2 - 2016 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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Cite this