TIM-8122 V1

Venkatachary, S. K., Alagappan, A., & Andrews, L. J. B. (2021). Cybersecurity challenges in energy sector (virtual power plants)—Can edge computing principles be applied to enhance security? Energy Informatics, 4(1). https://doi.org/10.1186/s42162-021-00139-7
These authors present a solution for Distributed Energy Resources (DERs) using edge computing and AI.

Zhoukai Wang, Xinhong Hei, Weigang Ma, Yichuan Wang, Kan Wang, & Qiao Jia. (2022). Parallel anomaly detection algorithm for cybersecurity on the highspeed train control system. Mathematical Biosciences and Engineering, 19(1), 287–308. https://doi.org/10.3934/mbe.2022015
These authors discuss the high-speed rail system in China and the detection of anomalies using anomaly detection algorithms for cybersecurity.

Sokolov, M. P., Manatin, P. A., & Zyulyarkina, N. D. (2023). The parallelization of computations for ensuring information security in connected vehicle systems using q-effective programming: The example of dijkstra’s algorithm. 2023 International Russian Smart Industry Conference (SmartIndustryCon), 2023 International, 438–442. https://doi.org/10.1109/SmartIndustryCon57312.2023.10110745
These authors present computational improvements to ensure that information security in connected vehicle systems is parallelized.

Chang, C. Y., Lee, C. H., & Chiang, K. N. (2023). Using grid search methods and parallel computing to reduce ai training time for reliability lifetime prediction of wafer-level packaging. 2023 24th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), 2023 24th International Conference On, 1–5. https://doi.org/10.1109/EuroSimE56861.2023.10100751
The paper presents a grid search method leveraging parallel computing with the objective of reducing AI training time for reliability.