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%0 Thesis
%A Sen, Ömer
%T Detection of multi-stage cyberattacks on SCADA-based control systems in power grids
%I Rheinisch-Westfälische Technische Hochschule Aachen
%V Dissertation
%C Aachen
%M RWTH-2025-07372
%P 1 Online-Ressource : Illustrationen
%D 2025
%Z Veröffentlicht auf dem Publikationsserver der RWTH Aachen University
%Z Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2025
%X The ongoing digitization and transformation of power grids, driven by the integration of Information Technology (IT) and Operational Technology (OT), have introduced new cybersecurity challenges. Modern smart grids, increasingly reliant on interconnected components, are vulnerable to sophisticated multi-stage cyberattacks that can exploit weaknesses across IT and OT domains. This work addresses these challenges by proposing a comprehensive framework for detecting and mitigating coordinated cyberattacks in power grids. The framework incorporates a scalable simulation environment to model cyber-physical interactions, enabling the generation of synthetic datasets for robust detection model evaluation. It introduces advanced correlation techniques to infer attacker strategies and reconstruct attack sequences, thereby enhancing situational awareness and supporting proactive defense strategies. Key contributions of this work include the development of a reproducible methodology for generating multi-stage attack datasets, the implementation of domain-specific knowledge to improve detection accuracy, and an evaluation of detection mechanisms through extensive experimentation in simulated attack scenarios. Results demonstrate the framework’s ability to reliably detect complex attack sequences, adapt to varying attack patterns, and provide actionable insights for incident response. By leveraging hybrid simulation techniques and advanced correlation models, this research aims to strengthen smart grid resilience, offering a reproducible, adaptable platform for advancing cybersecurity in critical energy infrastructure.
%F PUB:(DE-HGF)11
%9 Dissertation / PhD Thesis
%R 10.18154/RWTH-2025-07372
%U https://publications.rwth-aachen.de/record/1017494