International Journal of Computational Intelligence Systems

Volume 12, Issue 1, November 2018, Pages 39 - 58

Deep Learning for Detection of Routing Attacks in the Internet of Things

Authors
Furkan Yusuf YAVUZ1, 2, furkan.yavuz@tubitak.gov.tr yusufyavuz@sehir.edu.tr, Devrim ÜNAL3, dunal@qu.edu.qa, Ensar GÜL2, ensargul@sehir.edu.tr
1BILGEM, TUBITAK, P.O.Box 74 Gebze, Kocaeli, 41470, TURKEY
2Dept. of Computer Engineering, Istanbul Sehir University, Dragos, Kartal, Istanbul, 34865, TURKEY
3KINDI Center for Computing Research, Qatar University, Doha, QATAR
Received 2 January 2018, Accepted 26 June 2018, Available Online 1 November 2018.
DOI
https://doi.org/10.2991/ijcis.2018.25905181How to use a DOI?
Keywords
deep learning; continuous monitoring; cyber-physical systems; cyber security
Abstract

Cyber threats are a showstopper for Internet of Things (IoT) has recently been used at an industrial scale. Network layer attacks on IoT can cause significant disruptions and loss of information. Among such attacks, routing attacks are especially hard to defend against because of the ad-hoc nature of IoT systems and resource constraints of IoT devices. Hence, an efficient approach for detecting and predicting IoT attacks is needed. Systems confidentiality, integrity and availability depends on continuous security and robustness against routing attacks. We propose a deep-learning based machine learning method for detection of routing attacks for IoT. In our study, the Cooja IoT simulator has been utilized for generation of high-fidelity attack data, within IoT networks ranging from 10 to 1000 nodes. We propose a highly scalable, deep-learning based attack detection methodology for detection of IoT routing attacks which are decreased rank, hello-flood and version number modification attacks, with high accuracy and precision. Application of deep learning for cyber-security in IoT requires the availability of substantial IoT attack data and we believe that the IoT attack dataset produced in this work can be utilized for further research.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 1
Pages
39 - 58
Publication Date
2018/11/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.2018.25905181How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Furkan Yusuf YAVUZ
AU  - Devrim ÜNAL
AU  - Ensar GÜL
PY  - 2018
DA  - 2018/11/01
TI  - Deep Learning for Detection of Routing Attacks in the Internet of Things
JO  - International Journal of Computational Intelligence Systems
SP  - 39
EP  - 58
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2018.25905181
DO  - https://doi.org/10.2991/ijcis.2018.25905181
ID  - YAVUZ2018
ER  -