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المؤلفYAVUZ, Furkan Yusuf
المؤلفUnal, Devrim
المؤلفGÜL, Ensar
تاريخ الإتاحة2020-02-24T08:57:11Z
تاريخ النشر2018
اسم المنشورInternational Journal of Computational Intelligence Systems
المصدرScopus
الرقم المعياري الدولي للكتاب18756891
معرّف المصادر الموحدhttp://dx.doi.org/10.2991/ijcis.2018.25905181
معرّف المصادر الموحدhttp://hdl.handle.net/10576/12967
الملخصCyber 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.
اللغةen
الناشرAtlantis Press
الموضوعContinuous monitoring
Cyber security
Cyber-physical systems
Deep learning
العنوانDeep learning for detection of routing attacks in the internet of things
النوعArticle
الصفحات39 - 58
رقم العدد1
رقم المجلد12
dc.accessType Abstract Only


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