A Holistic Cybersecurity Framework against False Data Injection Attacks in Smart Water Distribution Systems Employing Auto-Encoders
Publication: World Environmental and Water Resources Congress 2024
ABSTRACT
Escalating cyber threats targeting smart water distribution systems (SWDSs) have emerged as a pressing concern. In response, this paper introduces an unsupervised cybersecurity model leveraging auto-encoders (AE) to detect the SWDSs against false data injection attacks (FDIA). The model utilized simulated clean data for learning normal system behavior and underwent validation and testing against datasets containing malicious cyberattack events. Notably, the model effectively detected all FDIA cases in both datasets. Moreover, an extensive evaluation of its performance confirms its suitability for online detection as it exhibited no delays in detection. This robustness ensures that the Supervisory Control and Data Acquisition (SCADA) center is alerted to take action against cyberattacks. The proposed detector achieved impressive classification scores including a recall of 1.00, a precision of 0.89, and an F1 score of 0.91. The proposed technique is generalizable and applicable in real-time cyber surveillance in various smart cyber-physical systems.
Get full access to this chapter
View all available purchase options and get full access to this chapter.
REFERENCES
Lyngaas, S., and Sgueglia, K. (2023). “Federal officials investigating after pro-Iran group allegedly hacked water authority in Pennsylvania.” Accessed on November 30, 2023.
Addeen, H. H., Xiao, Y., Li, J., and Guizani, M. (2021). “A survey of cyber-physical attacks and detection methods in smart water distribution systems.” IEEE Access, 9, 99905–99921.
America’s Cyber Defence Agency. (2023). “Critical infrastructure sectors.” Accessed on July 26, 2023.
Bentley Systems, I. (2022). “Bentley OpenFlows WaterGEMS, <https://www.bentley.com/software/openflows-watergems/>.
Brentan, B. M., Luvizotto, E., Jr., Herrera, M., Izquierdo, J., and Pérez-García, R. (2017). “Hybrid regression model for near real-time urban water demand forecasting.” Journal of Computational and Applied Mathematics, 309, 532–541.
Dayhoff, J. E. (1990). Neural network architectures: an introduction. Van Nostrand Reinhold Co.
Hagan, M. T., Demuth, H. B., and Beale, M. (1997). Neural network design. PWS Publishing Co.
Kingma, D. P., and Ba, J. (2014). “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980.
Martinelli, M., Tronci, E., Dipoppa, G., and Balducelli, C. (2004). “Electric power system anomaly detection using neural networks.” International Conference on Knowledge-based and intelligent information and Engineering Systems, Springer, 1242–1248.
Nelson, N. (2022). “U.S. Water Utilities: Prime Cyberattack Target, Experts Warn. Accessed: 2022-10-03.
Potluri, S., and Diedrich, C. (2019). “Deep learning based efficient anomaly detection for securing process control systems against injection attacks.” 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 854–860.
Raza, N., and Moazeni, F. (2023). “Chance-constrained vulnerability assessment of smart water distribution systems against stealthy false data injection attacks.” International Journal of Critical Infrastructure Protection, 100645.
Sakurada, M., and Yairi, T. (2014). “Anomaly detection using autoencoders with non- linear dimensionality reduction.” Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis, 4–11.
Taormina, R., and Galelli, S. (2018). “Deep-learning approach to the detection and localization of cyber-physical attacks on water distribution systems.” Journal of Water Resources Planning and Management, 144(10), 04018065.
Taormina, R., Galelli, S., Tippenhauer, N. O., Salomons, E., Ostfeld, A., Eliades, D. G., Aghashahi, M., Sundararajan, R., Pourahmadi, M., and Banks, M. K. (2018). “Battle of the attack detection algorithms: Disclosing cyber attacks on water distri- bution networks.” Journal of Water Resources Planning and Management, 144(8), 04018048.
Tuptuk, N., Hazell, P., Watson, J., and Hailes, S. (2021). “A Systematic Review of the State of Cyber-Security in Water Systems.” Water, 13(1), 81 Number: 1 Publisher: Multidisciplinary Digital Publishing Institute.
Information & Authors
Information
Published In
World Environmental and Water Resources Congress 2024
Pages: 1087 - 1098
History
Published online: May 16, 2024
ASCE Technical Topics:
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.