Threat analysis and adversarial model for Smart Grids
<|reference_start|>Threat analysis and adversarial model for Smart Grids: The power grid is a critical infrastructure that allows for the efficient and robust generation, transmission, delivery and consumption of electricity. In the recent years, the physical components have been equipped with computing and network devices, which optimizes the operation and maintenance of the grid. The cyber domain of this smart power grid opens a new plethora of threats, which adds to classical threats on the physical domain. Accordingly, different stakeholders including regulation bodies, industry and academy, are making increasing efforts to provide security mechanisms to mitigate and reduce cyber-risks. Despite these efforts, there have been various cyberattacks that have affected the smart grid, leading in some cases to catastrophic consequences, showcasing that the industry might not be prepared for attacks from high profile adversaries. At the same time, recent work shows a lack of agreement among grid practitioners and academic experts on the feasibility and consequences of academic-proposed threats. This is in part due to inadequate simulation models which do not evaluate threats based on attackers full capabilities and goals. To address this gap, in this work we first analyze the main attack surfaces of the smart grid, and then conduct a threat analysis from the adversarial model perspective, including different levels of knowledge, goals, motivations and capabilities. To validate the model, we provide real-world examples of the potential capabilities by studying known vulnerabilities in critical components, and then analyzing existing cyber-attacks that have affected the smart grid, either directly or indirectly.<|reference_end|>
@article{ríos2024threat,
    title={Threat analysis and adversarial model for Smart Grids},
    author={Javier Sande R'ios, Jes'us Canal S'anchez, Carmen Manzano
  Hernandez, Sergio Pastrana},
    journal={arXiv preprint arXiv:2406.11716},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11716},
    primaryClass={cs.CR}
}