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Secure Extended Kalman Filter-Based State Estimation and PID Controller for Resilience Water Systems under Sensor False Data Injection Attacks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 34 |
| Year of Publication: 2025 |
| Authors: MD Masud Rana, Bo Sun |
10.5120/ijca2025925357
|
MD Masud Rana, Bo Sun . Secure Extended Kalman Filter-Based State Estimation and PID Controller for Resilience Water Systems under Sensor False Data Injection Attacks. International Journal of Computer Applications. 187, 34 ( Aug 2025), 1-5. DOI=10.5120/ijca2025925357
Abstract
Secure state estimation and optimal control in cyber-physical systems (CPS) such as interconnected water tanks are necessary to ensure reliability, safety, and stability under adversarial conditions. This paper identifies three key research challenges: (1) Develop an attack-resilient water level state estimation process under false data injection attacks, (2) Design an optimal controller for maintaining stability of water levels, and (3) Conduct extensive simulations to find a suitable solution for practical water system implementation under adversarial conditions. These are critical business issues, as water cannot be stored in a large- scale, however water is essential for daily life and industries. Therefore, it is important to know the water level observability through state estimation process, afterward we will need to apply the control framework to maintain the water level stability as an acceptable level. To address these important challenges, this paper proposes the Chi-square residual based extended Kalman filter algorithm for accurate water level estimation under adversarial conditions. Afterwards, the PID controller is adopted to maintain the stability of the water level at the reference position. Extensive simulations demonstrate that the proposed algorithm can estimate and maintain water level at an acceptable level in a short period of time. Hopefully, these contributions and findings can significantly help cybersecurity education, CPS secure control ecosystems, and water reservoir framework development.
References
- N. Tuptuk, P. Hazell, J. Watson, and S. Hailes, “A systematic review of the state of cyber-security in water systems,” Water, vol. 13, no. 1, p. 81, 2021.
- D. Ding, Q.-L. Han, X. Ge, and J. Wang, “Secure state estimation and control of cyber-physical systems: A survey,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 1, pp. 176–190, 2020.
- Y. H. Choi, A. Sadollah, and J. H. Kim, “Improvement of cyber-attack detection accuracy from urban water systems using extreme learning machine,” Applied Sciences, vol. 10, no. 22, p. 8179, 2020.
- H. Mahmoud, W. Wu, and M. M. Gaber, “A time-series selfsupervised learning approach to detection of cyber-physical attacks in water distribution systems,” Energies, vol. 15, no. 3, p. 914, 2022.
- X. Wang and V. Babovic, “Application of hybrid Kalman filter for improving water level forecast,” Journal of Hydroinformatics, vol. 18, no. 5, pp. 773–790, 2016.
- R. Adnan, F. A. Ruslan, and Z. M. Zain, “Extended Kalman Filter (EKF) prediction of flood water level,” in IEEE Control and System Graduate Research Colloquium. IEEE, 2012, pp. 171–174.
- B. Sun, X. Shan, K. Wu, and Y. Xiao, “Anomaly detection based secure in-network aggregation for wireless sensor networks,” IEEE Systems Journal, vol. 7, no. 1, pp. 13–25, 2012.
- X. Li, Z. Tian, and D. Lu, “Event-triggered protocol-based control for cyber-physical systems vulnerable to dual-channel DoS attacks,” IEEE Transactions on Control Systems Technology, vol. 33, no. 1, pp. 369–383, 2015.
- J. Fang, X. Cheng, H. Gai, S. Lin, and H. Lou, “Development of machine learning algorithms for predicting internal corrosion of crude oil and natural gas pipelines,” Computers & Chemical Engineering, vol. 177, p. 108358, 2023.
- L.-W. Mao and G.-H. Yang, “Optimal stealthy attack with side information against remote state estimation: A corrupted innovation-based strategy,” IEEE Transactions on Cybernetics, vol. 55, no. 2, 2025.
- A. Biskupovic, A. Villalonga, F. Casta˜no, R. E. Haber, and F. N´u˜nez, “Agent-based supervision for service-oriented industrial cyber-physical systems,” IEEE Transactions on Industrial Informatics, vol. 21, no. 3, pp. 2719–2728, 2025.
- A. Nezamzadeh and M. Esmaeilidehkordi, “Disturbance rejection in quadruple-tank system by proposing new method in reinforcement learning,” in IEEE 14th International Conference on Computer and Knowledge Engineering. IEEE, 2024, pp. 137–142.
- P. Griffioen, B. H. Krogh, and B. Sinopoli, “Ensuring resilience against stealthy attacks on cyber-physical systems,” IEEE Transactions on Automatic Control, vol. 69, no. 12, pp. 8234–8246, 2024.
- D. Wang, P. Wang, J. Zhou, L. Sun, B. Du, and Y. Fu, “Defending water treatment networks: Exploiting spatio-temporal effects for cyber attack detection,” in IEEE International Conference on Data Mining. IEEE, 2020, pp. 32–41.
- Q. Han, R. Eguchi, S. Mehrotra, and N. Venkatasubramanian, “Enabling state estimation for fault identification in water distribution systems under large disasters,” in IEEE 37th Symposium on Reliable Distributed Systems. IEEE, 2018, pp. 161–170.
- K. H. Johansson, “The quadruple-tank process: A multivariable laboratory process with an adjustable zero,” IEEE Transactions on Control Systems Technology, vol. 8, no. 3, pp. 456– 465, 2002.
- J. Xie, F. Bonassi, and R. Scattolini, “Learning control affine neural narx models for internal model control design,” IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 8137–8149, 2025.
- J. O. d. A. Limaverde Filho, M. T. de Sousa, A. B. Rodrigues, and E. Fortaleza, “A derivative-free Kalman filter-based disturbance observer using flat inputs,” in IEEE International Conference on Automation/XXVI Congress of the Chilean Association of Automatic Control. IEEE, 2024, pp. 1–6.
- O. F. A. Aal and R. Hemeimat, “Data-driven based decoupled control scheme for interacting quadruple tank process,” in IEEE 12th International Conference on Systems and Control. IEEE, 2024, pp. 149–154.
- A. Abughali, M. Alansari, and A. S. Al-Sumaiti, “Deep learning strategies for detecting and mitigating cyber-attacks targeting water-energy nexus,” IEEE Access, vol. 12, pp. 129 690–129 704, 2024.
- G. A. Terejanu et al., “Extended Kalman filter tutorial,” University at Buffalo, vol. 27, p. 6, 2008.
- Y. Takefuji, “Chi-square and P-values versus machine learning feature selection,” Annals of Oncology, vol. 36, no. 2, pp. 227–228, 2025.
- D. M. Bui, D. P. Le, and H. M. Nguyen, “Development of a novel backup fault protection algorithm for low-voltage DC microgrids based on local measurements and Chi-square statistics,” Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 15 106–15 120, 2024.
- D. Mikhaylenko and P. Zhang, “Robust mirror attacks on cyber-physical systems,” in IEEE 10th International Conference on Control, Decision and Information Technologies. IEEE, 2024, pp. 724–729.
- C. Ning and Z. Xi, “Improved stealthy false data injection attacks in networked control systems,” IEEE Systems Journal, vol. 18, no. 1, pp. 505–515, 2024.
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