Abstract
Modern technologies empower water distribution systems (WDS) for better services in the processes of water supply, storage, distribution, and recycling. They improve real-time monitoring, automating, and managing. However, the limitations of these technologies introduce cyber-physical attacks to the WDS. The main goals of cyber-physical attacks include disrupting normal operations and tampering the critical data, which have negative impacts on the WDS. Therefore, it is vital to develop and implement solutions to increase the security of the WDS by detecting and mitigating cyber-physical attacks. Since security for WDS is relatively new, there are no surveys on this topic despite its vital importance. Therefore, in this paper, we provide a comprehensive survey for the common cyber-physical attacks and common detection mechanisms for the WDS. We compare the attacks and detection methods with emphasis on ideas, methods, evaluation results, advantages, limitations, etc. We further provide a future research direction. We realize that there are still not many research attempts in this area and we hope that this work can trigger more research activities related to the WDS.
Highlights
Smart cities intend to improve the life quality of people and offer intelligent services by using smart devices
water distribution systems (WDS) is prone to cyber-physical attacks which can disrupt normal operations and tamper with the critical data of the water system
The Feed-Forward Neural Network model (FFNN) model algorithm trained with the Restricted Boltzmann Machine model (RBM) model including two layers achieves an accuracy rate of around 97%, while the FFNN model algorithm with the RBM model including three layers achieves an accuracy around 97%
Summary
Smart cities intend to improve the life quality of people and offer intelligent services by using smart devices. Cyber-physical systems and internet of things devices are considering as main components to build water distribution systems (WDS) The limitations of IoT resources reduce the security of WDS and attract attackers to disrupt normal operations and tamper with the critical data of the water system. In the second scenario, the attack targets to alter the water level reading that was collected by PLC3 in T4 It sends the altered information of PLC3 that controls PU6 and PU7 to give a wrong decision to deactivate pumps 6 and 7. In the fourth scenario, the attack aims to hack the communication link between PLC2 and SCADA system, and to alter the data reading of T1 water level. The fault diagnosis module needs to be enhanced with a robust detection method to increase the security of the water system testbed
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