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Application of Deep Learning on Cyber-Attack Detection and Identification in Industrial Control Systems

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dc.contributor.advisor Karimipour, Hadis
dc.contributor.author Al-Abassi, Abdulrahman
dc.date.accessioned 2020-11-26T16:12:17Z
dc.date.available 2020-11-26T16:12:17Z
dc.date.copyright 2020-12
dc.date.created 2020-11-19
dc.date.issued 2020-05
dc.identifier.uri https://hdl.handle.net/10214/21378
dc.description.abstract Wide implementation of the communication networks in Industrial Control Systems (ICS) increases their vulnerability towards malicious attacks, which could cause potential devastating results. ICS use different control and network systems to monitor and control critical infrastructures through real-time monitoring to detect abnormal behaviors of the system. Unfortunately, traditional Intrusion Detection Systems (IDS) are mainly developed to support IT systems and are often not applicable in industrial environments. The main goal of this thesis is to analyze network traffic and sensory measurements in real- ICS and use different learning-based techniques to detect and locate cyber-attacks. To do so, several learning-based models, including an ensemble deep learning-based cyber-attack detection algorithm for imbalanced ICS datasets as well as a self-tuning and scalable deep learning and classification models for cyber-attack location identification, are proposed. The performance of the proposed models is evaluated using two real ICS datasets. The results show that the proposed models outperform state-of-the-art works in the literature, in f1-score, accuracy, and Matthews Correlation Coefficient (MCC).   en_US
dc.language.iso en en_US
dc.publisher IEEE Access en_US
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject Industrial Control Systems en_US
dc.subject Intrusion Detection Systems en_US
dc.subject Cyber Physical Systems en_US
dc.subject Machine Learning en_US
dc.subject Internet Protocol en_US
dc.subject Supervisory Control and Data Acquisition en_US
dc.subject Internet of Things en_US
dc.subject Information Technology en_US
dc.subject Operation Technology en_US
dc.subject Autoencoder en_US
dc.title Application of Deep Learning on Cyber-Attack Detection and Identification in Industrial Control Systems en_US
dc.type Thesis en_US
dc.degree.programme Engineering en_US
dc.degree.name Master of Applied Science en_US
dc.degree.department School of Engineering en_US
dc.rights.license All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dcterms.relation Al-Abassi, H. Karimipour, A. Dehghantanha and R. M. Parizi, “An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System”, IEEE Access, vol. 8, pp. 83965-83973, May. 2020, DOI: 10.1109/ACCESS.2020.2992249 en_US


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