Strengthening Water Utility Cybersecurity: Evaluating Detection Algorithms with BATADAL

With increased levels of digitalization and networked systems, maintaining the security of water utilities has gained prime importance. Digital infrastructure has tremendously improved operational efficiency for the water system but exposed its critical systems to various cyber threats. The cybersecurity of water utilities would alleviate the risks of water supply contamination, service disruption, and economic consequences. Detection algorithm evaluation is thus a crucial step in strengthening security measurements. The Battle of the Attack Detection Algorithms BATADAL gives an exclusive opportunity to rigorously test the strength of several detection algorithms designed to apply to water systems. Realistic cyber-attacks simulated through BATADAL will go a long way in rigorous testing and evaluation so researchers and practitioners can pinpoint the most potent detection strategies. A comparative evaluation for the precision of Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) shows that SVM is superior to RF and DT because of its robustness in dealing with high-dimensional data. Precision is high since the decision boundary is optimized for SVM, so the best performance is in SVM among the three, and the observed accuracy is 97.09. This enhances the water infrastructure's ability and safeguards vital resources while ensuring the community's safe water supply is not interrupted.