DOI: https://doi.org/10.32515/2664-262X.2025.12(43).2.36-54
Cloud-based Technology for Monitoring Key Performance Indicators of Technological Processes in Critical Infrastructure
About the Authors
Tetiana Smirnova, Candidate of Science (Engineering), Senior Lecturer, Department of Automation of Production Processes, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine ORCID: https://orcid.org/0000-0001-6896-0612, e-mail: sm.tetyana@gmail.com
Kostiantyn Buravchenko, Associate Professor, PhD (candidate of technical sciences), Associate Professor of Cybersecurity & Software Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0001-6195-7533, e-mail: buravchenkok@gmail.com.
Oleksandr Dobrynchuk, Junior Researcher, State University "Kyiv Aviation Institute", Kyiv, Ukraine, ORCID: https://orcid.org/0009-0002-2877-844X, e-mail: Dobrynchuk85@icloud.com.
Serhii Smirnov, Associate Professor, PhD, Associate Professor of Cybersecurity & Software Academic Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-7649-7442, e-mail: smirnov.ser.81@gmail.com.
Nataliia Yakymenko, Associate Professor, PhD (Candidate of Physical and Mathematical Sciences), Associate Professor of Cybersecurity & Software Academic Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4498-0093, e-mail: yakimenko_n_m@ukr.net
Oleksii Smirnov, Professor, Doctor of technical sciences, head of Cybersecurity & Software Academic Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0001-9543-874X, e-mail: dr.smirnovoa@gmail.com.
Abstract
In this work, a cloud technology was developed for monitoring key performance indicators of critical infrastructure technological processes in real time in order to detect deviations in technological processes, as well as to prevent attacks (failures) by analyzing anomalous equipment behavior, changes in load modes, resource consumption, etc. The tuple model of key performance indicators was further developed, which allows systematizing monitoring parameters in information and communication systems of critical infrastructure objects, formalizing automatic data processing, supporting integration with analytical cloud platforms, as well as identifying deviations (anomalies), cyber incidents, degradation, excessive load or sabotage and, as a result, preparing aggregated key performance indicators for daily monitoring by operators and IT services. A model of the technological process of electric arc processing in UAV engineering is proposed, which in the conditions of the current stage of the Russian-Ukrainian war is part of the critical infrastructure in the economy and defense- industrial production sector. This is due to the fact that UAVs now play a crucial role on the battlefield. A scheme for monitoring key performance indicators for the electric arc processing technological process has been developed. For this, the use of cloud technologies is proposed, a general scheme of the electric arc processing technological process in UAV engineering is given, with their use. An example of a model infrastructure based on the Azure cloud platform has been implemented. The data model is presented in JSON format (which is effective for API, MQTT broker or Kafka). Verification of the model on empirical data confirmed its compliance with the requirements, resistance to changing conditions and great significance for monitoring technological processes in critical infrastructure. In the context of digitalization and countering cyber risks, the model is the basis for creating a digital twin of the production system. Thus, reliability, predictability and security are ensured. The paper proposes integration with artificial intelligence/machine learning (AI/ML) algorithms, such as LSTM, which allows for real-time advanced analytics, adaptive process control, and automated decision- making. In the future, the LSTM algorithm can be used to build a model for predicting the parameters of key performance indicators in an electric arc processing system, and other AI/ML and data mining algorithms that can process large volumes of time series and take into account complex nonlinear dependencies between technological parameters can also be used.
Keywords
cloud technology, technological processes, critical infrastructure, information and communication system, electric arc processing, UAV, cyber risks, cybersecurity, IoT, artificial intelligence, machine learning
Cloud-based Technology for Monitoring Key Performance Indicators of Technological Processes in Critical Infrastructure
About the Authors
Tetiana Smirnova, Candidate of Science (Engineering), Senior Lecturer, Department of Automation of Production Processes, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine ORCID: https://orcid.org/0000-0001-6896-0612, e-mail: sm.tetyana@gmail.com
Kostiantyn Buravchenko, Associate Professor, PhD (candidate of technical sciences), Associate Professor of Cybersecurity & Software Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0001-6195-7533, e-mail: buravchenkok@gmail.com.
Oleksandr Dobrynchuk, Junior Researcher, State University "Kyiv Aviation Institute", Kyiv, Ukraine, ORCID: https://orcid.org/0009-0002-2877-844X, e-mail: Dobrynchuk85@icloud.com.
Serhii Smirnov, Associate Professor, PhD, Associate Professor of Cybersecurity & Software Academic Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-7649-7442, e-mail: smirnov.ser.81@gmail.com.
Nataliia Yakymenko, Associate Professor, PhD (Candidate of Physical and Mathematical Sciences), Associate Professor of Cybersecurity & Software Academic Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4498-0093, e-mail: yakimenko_n_m@ukr.net
Oleksii Smirnov, Professor, Doctor of technical sciences, head of Cybersecurity & Software Academic Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0001-9543-874X, e-mail: dr.smirnovoa@gmail.com.
Abstract
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Citations
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27. Kuznetsov O., Smirnov O., Kuznetsova T. et al. Privacy-utility trade-offs in IoT networks: A comparative analysis of differential privacy mechanisms. Security and Privacy of Cyber Physical Systems Emerging Trends Technologies and Applications. 2025. P. 589–622.
28. Kuznetsov O., Smirnov O., Akhmetov B. et al. Deep Learning Frontiers in Copy-Move Forgery Detection: Advances, Challenges, and Future Directions. Advancements in Cybersecurity Next Generation Systems and Applications. 2025. P. 202–229.
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31. Smirnov O., Sydorenko V., Aleksander M. et al. Simulation of the cloud IoT-based monitoring system for critical infrastructures. CEUR Workshop Proceedings. 2023. Vol. 3530. P. 256–265.
Copyright (©) 2025, Tetiana Smirnova, Kostiantyn Buravchenko, Oleksandr Dobrynchuk, Serhii Smirnov, Nataliia Yakymenko, Oleksii Smirnov