Deep Learning for Detection of Routing Attacks in the Internet of Things
- 10.2991/ijcis.2018.25905181How to use a DOI?
- deep learning; continuous monitoring; cyber-physical systems; cyber security
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.
- © 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 - 10.2991/ijcis.2018.25905181 ID - YAVUZ2018 ER -