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AuthorYAVUZ, Furkan Yusuf
AuthorÜNAL, Devrim
AuthorGÜL, Ensar
Available date2020-02-24T08:57:11Z
Publication Date2018
Publication NameInternational Journal of Computational Intelligence Systems
ISSN18756891
URIhttp://dx.doi.org/10.2991/ijcis.2018.25905181
URIhttp://hdl.handle.net/10576/12967
AbstractCyber 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.
Languageen
PublisherAtlantis Press
SubjectContinuous monitoring
SubjectCyber security
SubjectCyber-physical systems
SubjectDeep learning
TitleDeep learning for detection of routing attacks in the internet of things
TypeArticle
Pagination39 - 58
Issue Number1
Volume Number12


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