A Novel Distributed Smart Water IoT-SCADA Simulation Testbed for Graph-Temporal AI Detection of Cross-Zone Process–Perception Dissociation Attacks

Smart water infrastructure increasingly relies on distributed Industrial Internet of Things (IIoT) and SCADA architectures for real-time monitoring, supervisory control, and operational decision-making. However, this integration also creates cyber-physical vulnerabilities in which attackers can maintain plausible communication and supervisory feedback while the underlying process gradually deviates from its true state. This paper presents a distributed smart water IoT-SCADA simulation testbed for investigating Cross-Zone Process–Perception Dissociation Attacks (CZPPDAs), where apparently normal SCADA/HMI information conceals harmful physical-process drift. The testbed spans external threat, perimeter, DMZ, enterprise IT, supervisory control, OT control, field process, and monitoring/data-acquisition layers, supporting realistic attacker progression, telemetry interception, semantic manipulation, supervisory deception, and concealed process deviation. A hybrid cyber-physical dataset is generated from communication descriptors, protocol variables, supervisory states, controller attributes, process measurements, and cross-zone consistency indicators. For detection, CZSD-Net models SCADA, HMI, PLC, RTU, and gateway components as an industrial dependency graph and learns evolving process–perception inconsistency through relation-aware graph encoding, dissociation reasoning, and temporal fusion. Experimental evaluation shows progressive improvement over baseline graph-learning configurations. The final model achieves 97.94% testing accuracy, 97.62% precision, 97.29% recall, and 97.43% F1-score. These results show that the proposed testbed and graph-temporal framework provide an effective approach for studying and detecting stealthy cyber-physical deception in smart water IoT-SCADA environments. It also supports reproducible evaluation of detection behavior across synchronized supervisory, control, network, and physical-process observations.