| Yazarlar (3) |
Furkan Yusuf Yavuz
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, Türkiye |
|
Devrim Ünal
Kındı Center For Computing Research, College Of Engineering, Qatar University, Türkiye |
Prof. Dr. Ensar GÜL
İstanbul Şehir Üniversitesi, Türkiye |
| Özet |
| 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. |
| Anahtar Kelimeler |
| deep learning | continuous monitoring | cyber-physical systems | cyber security |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS |
| Dergi ISSN | 1875-6891 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI |
| Dergi Grubu | Q4 |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2018 |
| Cilt No | 12 |
| Sayı | 1 |
| Sayfalar | 39 / 58 |
| Doi Numarası | 10.2991/ijcis.2018.25905181 |
| Makale Linki | https://www.atlantis-press.com/journals/ijcis/25905181 |
| Atıf Sayıları | |
| WoS | 101 |
| SCOPUS | 158 |
| Google Scholar | 206 |
