A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems

In light of the advancement technologies used in industrial control systems, securing their operation has become crucial, primarily since activity is consistently associated with integral elements related to environment, safety and health people, economy, many others. This work presents a distributed, machine learning based attack detection mitigation framework for sensor false data injection cyber-physical attacks systems. It developed using system’s standard operational validated hybrid testbed reverse osmosis plant. A MATLAB/Simulink-based simulation model process actual from local plant used. The system implemented Siemens S7-1200 programmable logic controllers 200SP Distributed Input/Output modules. proposed solution can be adopted existing systems demonstrated effective performance real-time launched by compromising communication links between controllers.