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Machine Learning-based Anomaly Detection in Industrial Control Systems
 Tsymbal, Kateryna ; Holasová, Eva (referee) ; Pospíšil, Ondřej (advisor)
The main goal of this thesis is to design a system for anomaly and intrusion detection in industrial control systems using machine learning. The theoretical part of the thesis provides a basic theoretical overview of industrial control systems and their security. Furthermore, knowledge about anomaly detection techniques and potential challenges in this area are discussed. Lastly, the theoretical part has reviewed various solutions for anomaly detection in industrial control systems using machine learning. In the practical part, machine learning algorithms are applied to the selected HAI dataset. Finally, the findings on the suitability of the used algorithms and the possibilities for further research are summarized. The purpose of this thesis is to improve the security of industrial control systems, and the results can serve as a basis for the future development of more effective methods for anomaly detection in this area.
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