ŞEHİR e-arşiv

Sensor based cyber attack detections in critical infrastructures using deep learning algorithms

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dc.contributor.author Yılmaz, Murat
dc.contributor.author Çatak, Ferhat Özgür
dc.contributor.author Gül, Ensar
dc.date.accessioned 2019-07-05T11:30:46Z
dc.date.available 2019-07-05T11:30:46Z
dc.date.issued 2019
dc.identifier.citation Yilmaz, M., Catak, F.O., Gul, E. (2019). Sensor based cyber attack detections in critical infrastructures using deep learning algorithms. Computer Science, 20(2), 213-244. doi: 10.7494/csci.2019.20.2.3191 en_US
dc.identifier.issn 1508-2806
dc.identifier.uri http://hdl.handle.net/11498/56228
dc.description.abstract The technology that has evolved with innovations in the digital world has also caused an increase in many security problems. Day by day, the methods and forms of cyberattacks are becoming more complicated; therefore, their detection has become more difficult. In this work, we have used datasets that have been prepared in collaboration with the Raymond Borges and Oak Ridge National Laboratories. These datasets include measurements of the Industrial Control Systems related to chewing attack behavior. These measurements include synchronized measurements and data records from Snort and relays with a simulated control panel. In this study, we developed two models using these datasets. The first is a model we call the DNN model, which was build using the latest deep learning algorithms. The second model was created by adding the AutoEncoder structure to the DNN model. All of the variables used when developing our models were set parametrically. A number of variables such as the activation method, the number of hidden layers in the model, the number of nodes in the layers, and the number of iterations were analyzed to create the optimum model design. When we run our model with optimum settings, we obtained better results than those found in related studies. The learning speed of the model has a 100% accuracy rate, which is also entirely satisfactory. While the training period of the dataset containing about 4 thousand different operations lasts for about 90 seconds, the developed model completes the learning process at a level of milliseconds to detect new attacks. This increases the applicability of the model in the real-world environment. en_US
dc.language.iso eng en_US
dc.publisher Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie en_US
dc.relation.isversionof 10.7494/csci.2019.20.2.3191 v en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Industrial Systems en_US
dc.subject Information Security en_US
dc.subject Cyber Security en_US
dc.subject Cyberattack Detections en_US
dc.subject Endüstriyel Sistemler en_US
dc.subject Bilgi Güvenliği en_US
dc.subject Siber Güvenlik en_US
dc.subject Siber Saldırı Algılamaları en_US
dc.title Sensor based cyber attack detections in critical infrastructures using deep learning algorithms en_US
dc.type Article en_US
dc.contributor.authorID 8176 en_US
dc.relation.journal Computer Science en_US
dc.identifier.volume 20 en_US
dc.identifier.issue 2 en_US
dc.identifier.endpage 244 en_US
dc.identifier.startpage 213 en_US


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