A Novel Approach for Detection and Location of Cyber-Attacks in Water Distribution Networks

Most scientific contributions addressing cyber-security issues in water distribution networks present proposals of detection systems and very few propose location systems. A novel methodology for detection and location of cyber-attacks in water distribution networks (WDNs) is proposed in this paper. Structural analysis and autoencoder neural networks are effectively combined with a the control chart Adaptive Exponentially Weighted Moving Average (AEWMA). In the training phase, the proposed detection and location framework only requires data from normal operating conditions and knowledge about the behavioral model of the system which represents an advantage over previous works that demand for additional data of cyber-attacks. Among other advantages of the proposed methodology are the high performance in the effective, robust and early detection and the effectiveness of the location strategy. The proposal was evaluated with the known case study BATADAL.