h1

% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Sen:1017494,
      author       = {Sen, Ömer},
      othercontributors = {Ulbig, Andreas and Henze, Martin},
      title        = {{D}etection of multi-stage cyberattacks on {SCADA}-based
                      control systems in power grids},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-07372},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2025},
      abstract     = {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.},
      cin          = {614010},
      ddc          = {004},
      cid          = {$I:(DE-82)614010_20200506$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2025-07372},
      url          = {https://publications.rwth-aachen.de/record/1017494},
}