Proximity-based anomaly detection in Securing Water Treatment

Industrial Control Systems (ICSs) utilize different sensors and various embedded systems to operate. Devices often communicate using protocols like Siemens Step 7 and Modbus, which were designed for use in closed networks many years ago and are vulnerable to attacks. The goal of this study is to detect anomalies in industrial control systems using a proximity-based approach on the Securing Water Treatment (SWaT) dataset. We encoded categorical data using one hot encoding and normalized numerical data using min max scaling. The experiment shown that by adopting a proximity-based approach, we can obtain state-of-the-art 99% precision and 98% recall and able to identify 35 out of 37 attack points, indicating that the suggested methodology is suitable for usage in industrial control system scenarios.