A Convolutional Neural Network-LSTM Based Physical Sensor Anomaly Detector for Interdependent SCADA Controllers

This study outlines a novel intrusion detection system (IDS) to detect compromised sensor data anomalies in interdependent industrial processes. The IDS used a peer-to-peer communication framework which allowed multiple programmable logic controllers (PLCs) to communicate and share sensor data. Utilizing the shared sensor data, state estimators used a long short-term memory (LSTM) machine learning algorithm to identify anomalous sensor readings connected to neighboring PLCs controlling an interdependent physical process. This study evaluated the performance of the IDS on three industrial operations aligning to a midstream oil terminal. The framework successfully detected several multi-sensor compromises during mid-stream oil terminal operations. A set of performance evaluations also showed no impact on the real-time operations of the PLC and outlined the prediction latencies of the framework.