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

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.

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Publication Details

Journal
IEEE Internet of Things Magazine
Published
2024-11-01
DOI
https://doi.org/10.1109/iotm.001.2300275
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Mohamed Medhat Gaber, Haitham Mahmoud, Wenyan Wu, Yonghao Wang
IEEE Internet of Things Magazine
Anomaly Detection Techniques and Applications
article

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

Mohamed Medhat Gaber, Haitham Mahmoud, Wenyan Wu, Yonghao Wang
article en

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.

IEEE Internet of Things MagazineVol. 7(6)
Birmingham City University (GB)
Horizon 2020 Framework Programme
Openalex Percentile: Top 74%
Anomaly Detection Techniques and Applications
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Enhancing Resilience in IoT Water Systems Using Data-Intelligence and Decentralization — Mohamed Medhat Gaber, Haitham Mahmoud, et al. · IEEE Internet of Things Magazine (2024) | TGRS Research Map | TGRS