Towards Automated Generation of Smart Grid Cyber Range for Cybersecurity Experiments and Training
<|reference_start|>Towards Automated Generation of Smart Grid Cyber Range for Cybersecurity Experiments and Training: Assurance of cybersecurity is crucial to ensure dependability and resilience of smart power grid systems. In order to evaluate the impact of potential cyber attacks, to assess deployability and effectiveness of cybersecurity measures, and to enable hands-on exercise and training of personals, an interactive, virtual environment that emulates the behaviour of a smart grid system, namely smart grid cyber range, has been demanded by industry players as well as academia. A smart grid cyber range is typically implemented as a combination of cyber system emulation, which allows interactivity, and physical system (i.e., power grid) simulation that are tightly coupled for consistent cyber and physical behaviours. However, its design and implementation require intensive expertise and efforts in cyber and physical aspects of smart power systems as well as software/system engineering. While many industry players, including power grid operators, device vendors, research and education sectors are interested, availability of the smart grid cyber range is limited to a small number of research labs. To address this challenge, we have developed a framework for modelling a smart grid cyber range using an XML-based language, called SG-ML, and for "compiling" the model into an operational cyber range with minimal engineering efforts. The modelling language includes standardized schema from IEC 61850 and IEC 61131, which allows industry players to utilize their existing configurations. The SG-ML framework aims at making a smart grid cyber range available to broader user bases to facilitate cybersecurity R\&D and hands-on exercises.<|reference_end|>
@article{mashima2024towards,
    title={Towards Automated Generation of Smart Grid Cyber Range for Cybersecurity
  Experiments and Training},
    author={Daisuke Mashima, Muhammad M. Roomi, Bennet Ng, Zbigniew Kalbarczyk,
  S.M. Suhail Hussain, Ee-chien Chang},
    journal={arXiv preprint arXiv:2404.00869},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00869},
    primaryClass={cs.CR}
}