Version 1
: Received: 19 December 2023 / Approved: 19 December 2023 / Online: 19 December 2023 (06:01:34 CET)
How to cite:
Kim, M.; Jeon, S.; Cho, J.; Gong, S. Data-Driven ICS Network Simulation for Synthetic Data Generation. Preprints2023, 2023121391. https://doi.org/10.20944/preprints202312.1391.v1
Kim, M.; Jeon, S.; Cho, J.; Gong, S. Data-Driven ICS Network Simulation for Synthetic Data Generation. Preprints 2023, 2023121391. https://doi.org/10.20944/preprints202312.1391.v1
Kim, M.; Jeon, S.; Cho, J.; Gong, S. Data-Driven ICS Network Simulation for Synthetic Data Generation. Preprints2023, 2023121391. https://doi.org/10.20944/preprints202312.1391.v1
APA Style
Kim, M., Jeon, S., Cho, J., & Gong, S. (2023). Data-Driven ICS Network Simulation for Synthetic Data Generation. Preprints. https://doi.org/10.20944/preprints202312.1391.v1
Chicago/Turabian Style
Kim, M., Jake Cho and Seonghyeon Gong. 2023 "Data-Driven ICS Network Simulation for Synthetic Data Generation" Preprints. https://doi.org/10.20944/preprints202312.1391.v1
Abstract
Industrial Control Systems (ICS) are integral to managing and optimizing processes in various industries, including manufacturing, power generation, and more. However, the scarcity of widely adopted ICS datasets hampers research efforts in areas like optimization and security. This scarcity arises due to the substantial cost and technical expertise required to create physical ICS environments. In response to these challenges, this paper presents a groundbreaking approach to generating synthetic ICS data through a data-driven ICS network simulation. We circumvent the need for expensive hardware by recreating the entire ICS environment in software. Moreover, rather than manually replicating the control logic of ICS components, we leverage existing data to autonomously generate control logic. The core of our method involves the stochastic setting of setpoints, which introduces randomness into the generated data. Setpoints serve as target values for controlling the operation of the ICS process. This approach enables us to augment existing ICS datasets and cater to the data requirements of machine learning-based ICS intrusion detection systems and other data-driven applications. Our simulated ICS environment employs virtualized containers to mimic the behavior of real-world PLCs and SCADA systems, while control logic is deduced from publicly available ICS datasets. Setpoints are generated probabilistically to ensure data diversity. Experimental results validate the fidelity of our synthetic data, emphasizing its ability to closely replicate temporal and statistical characteristics of real-world ICS networks. In conclusion, this innovative data-driven ICS network simulation offers a cost-effective and scalable solution for generating synthetic ICS data. It empowers researchers in the field of ICS optimization and security with diverse, realistic datasets, furthering advancements in this critical domain. Future work may involve refining the simulation model and exploring additional applications for synthetic ICS data.
Keywords
Industrial Control Systems (ICS); Synthetic Data Generation; Data-Driven Simulation; Machine Learning; Cybersecurity
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
