Enhancing Resilience in IoT Water Systems Using Data-Intelligence and Decentralization

Mahmoud, Haitham and Wu, Wenyan and Gaber, Mohamed Medhat and Wang, Yonghao (2025) Enhancing Resilience in IoT Water Systems Using Data-Intelligence and Decentralization. IEEE Internet of Things Magazine, 7 (6). pp. 44-51. ISSN 2576-3180

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Abstract

In recent years, concerns regarding the security of water networks have escalated due to the increasing integration of water assets (actuators and sensors) with the Internet, combining Information Technology (IT) and Operation Technology (OT). This integration promises improved services for water networks but also introduces the risk of cyber-attacks and physical threats. As a result, there is a growing need for novel security measures to protect integrated Cyber-Physical Systems (CPS) in water distribution systems (WDSs). This article assesses actual incidents and potential Cyber-Physical (CP) attacks on water systems, explores their operational impacts, and suggests mitigating measures. It introduces a secure architecture for an integrated CPS in WDS. The study incorporates attack detection and data validation models to enhance system robustness and reduce risks, adhering to the security criteria of Water 4.0. First, the attack detection model utilizes a two-stage architecture employing six Machine-Learning (ML) algorithms, resulting in developing a simulation model with the best-suited configuration. Second, the data validation model uses blockchain technology on transmitted data, creating a simulation model for water consumption data with various input types, consensus mechanisms, and data output conversion methods. Finally, this article provides a foundation for researchers, professionals, and operators in the water sector to experiment with, evaluate, and further develop this secure architecture for their water systems. Simulating their networks using the proposed architecture allows them to identify the most suitable configurations and parameters for their specific implementations.

Item Type:	Article
Identification Number:	10.1109/IOTM.001.2300275
Dates:	Date Event 1 November 2024 Accepted 24 March 2025 Published Online
Uncontrolled Keywords:	Cloud computing, Reviews, Machine learning, Data models, Threat assessment, Robustness, Sensors, Internet of Things, Information technology, Resilience
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Gemma Tonks
Date Deposited:	02 May 2025 12:39
Last Modified:	02 May 2025 12:43
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16332

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