SYSTEMATIC REVIEW
Cybersecurity in Water Distribution Networks: A Systematic Review of AI-Based Detection Algorithms
Water Distribution Networks (WDNs) are critical infrastructure for delivering clean and safe drinking water. As modern WDNs increasingly integrate cyber technologies, they evolve into complex cyber-physical systems (CPSs). This connectivity, however, introduces new vulnerabilities, including cyberattacks. Cybersecurity protects systems from unauthorized access, attacks, and data breaches. In this systematic review, we adopted the PRISMA 2020 reporting guideline. Predefined keyword strings were designed to extract relevant articles from Scopus and Web of Science during the period of 2014–2025. In total, 32 peer-reviewed studies were included for narrative synthesis following duplication and eligibility screening. The review protocol was not registered. This review provides a unified perspective on how Artificial Intelligence (AI) contributes to WDNs resilience. The literature is evaluated in terms of detection tasks, data modalities, learning paradigms, and model architecture. The results highlight three key findings: (a) data bias, reflected in significant reliance on specific synthetic datasets and limited use of real-world utility network data; (b) performance, with deep learning architecture, such as long-short-term memory models, achieving commendable levels of accuracy in intrusion detection, however, overall comparison with other models remain scenario-dependent; and (c) future directions, synthesized through an AI-centered perspective that emphasizes resilience and identifies research gaps in adaptive online learning, attack prediction, interpretability, federated learning and topology localization. This study concludes with recommendations for the broader integration of AI tools to support resilient WDN operation.
Key Executive Impact Metrics
Our analysis reveals the following critical performance indicators and trends for AI-based cybersecurity in WDNs:
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Studies Reviewed
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Accuracy (DL Models)
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Attack Detection Rate
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Deep Learning Dominance
95% Max Accuracy for Intrusion Detection| Algorithm Category | Key Characteristics | Strengths | Limitations |
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| Hybrid/Physics-Guided |
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Deep H₂O Framework Impact
The study reported improved generalizability and successful identification of poisoned data compared to classical methods. A residual LSTM model was employed within a coupled water-power grid test system, achieving detection metrics exceeding 96% across accuracy, precision, recall, F1-score, and AUC. Notably, this LSTM-based detector reduced the impact of stealthy attacks on water services by approximately 88% under real-time operation, highlighting its resilience benefits.
SCADA Data Focus
54% Studies Relying on Synthetic/Testbed DataEnterprise Process Flow
Data Collection & Preprocessing
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Model Training (Supervised/Unsupervised)
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Anomaly Detection
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Localization & Prioritization
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Operator Alert & Response
Anomaly Detection in Milan Smart Water Meter Data
Predescu et al. [27] applied a hybrid strategy to smart water meter data from a district in Milan by integrating unsupervised clustering with fault sensitivity analysis. This approach was shown to be effective in detecting distribution anomalies and significantly improved localization accuracy within the network.
Resilience Integration Gap
15 Studies Addressing Resilience (out of 32)Enterprise Process Flow
Threat & Attack Generation
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Detection & Diagnosis
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Hydraulic & Quality Impact
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Response & Recovery
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Resilience Assessment & Learning
| Dataset Tier | Performance Metrics | Resilience Relevance | Generalizability |
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| Tier 1 (Synthetic/Simulated) |
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| Tier 2 (Testbed) |
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| Tier 3 (Real Utility Data) |
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Calculate Your Potential ROI
Estimate the financial and operational benefits of implementing AI-driven cybersecurity solutions in your WDN.
Your AI Implementation Roadmap
A phased approach to integrating AI-driven cybersecurity into your Water Distribution Network for maximum impact.
Phase 1: Assessment & Strategy
Conduct a comprehensive vulnerability assessment of your WDN, identify key cyber-physical attack vectors, and define AI integration objectives. Develop a tailored strategy for data collection, model selection, and cybersecurity framework alignment.
Phase 2: Data & Infrastructure Preparation
Establish robust data pipelines for SCADA, sensor, and network traffic data. Implement data anonymization and privacy measures. Prepare testbed environments for model training and validation, focusing on dataset realism and diversity.
Phase 3: AI Model Development & Integration
Develop and train AI detection algorithms (e.g., deep learning models for intrusion, hybrid models for anomaly detection). Integrate these models with existing WDN operational systems, ensuring real-time monitoring capabilities and minimal latency.
Phase 4: Testing, Validation & Optimization
Rigorously test AI models against simulated and real-world attack scenarios, evaluating accuracy, precision, recall, and F1-score. Continuously fine-tune algorithms, address false positives, and validate performance in dynamic WDN conditions. Incorporate interpretability tools.
Phase 5: Deployment & Resilience Enhancement
Deploy AI-driven cybersecurity solutions across your WDN. Establish a resilience-aware framework to link detection with mitigation strategies and recovery actions. Implement adaptive learning mechanisms for continuous improvement and long-term WDN security.
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