Dual-Approach Machine Learning for Robust Cyber-Attack Detection in Water Distribution System

Water Distribution Systems (WDSs) are vital infrastructures responsible for delivering clean and safe water to various sectors.However, advancements in WDSs infrastructure, such as automated monitoring and control systems, have made these systems vulnerable to cyberattacks.These attacks can manipulate the system, causing severe disruptions like contaminating water supplies or disrupting water flow.To protect WDSs from such threats, a strong and reliable detection system is essential.This study proposes a novel approach that combines classification and regression models for anomaly detection using the BATADAL dataset.Initially, a classification model predicts whether an attack is occurring.Subsequently, a regression model evaluates water levels to determine if they are within normal ranges.If an anomaly is detected by either approach, the framework raises an attack alert.In this framework, the Extra Trees Classifier demonstrated significant performance, achieving an accuracy of 0.89 in the classification task and an average 2 score of 0.6658 in the regression task.Overall, the combined proposed framework delivered impressive results, achieving significant accuracy in detecting anomalies.