A TESTBED FOR EVALUATION OF MACHINE LEARNING TECHNIQUES IN ANOMALY DETECTION IN INDUSTRIAL CYBER PHYSICAL SYSTEMS

The increasing digitalization of industrial environments, driven by Industry 4.0, has fostered the integration of Cyber-Physical Systems (CPS), which combine Information Technology (IT) and Operational Technology (OT) to enhance efficiency, automation, and real-time decision-making. However, this convergence has also increased the exposure of Industrial Control Systems (ICS) to cyber threats, highlighting the need for tools and frameworks capable of detecting anomalies and cyberattacks. In this context, the present work proposes a testbed for evaluating Artificial Intelligence techniques aimed at the automatic detection of anomalies in industrial processes. The testbed enables the simulation of both normal and anomalous conditions following the GEMMA methodology. A use case is presented in which a supervised machine learning approach—specifically, the K-Nearest Neighbours (KNN), Random Forest (RF), and Support Vector Machine (SVM) algorithms—is applied to data generated in the Testbed for Anomaly Detection and Protection of Industrial Cyber-Physical Systems (DAyPSCI). The results demonstrate the feasibility of the proposed approach for classifying instances based on their operational behaviour, achieving high accuracy in identifying normal patterns and actuator failures, and acceptable performance in detecting sensor anomalies. These findings validate the testbed as a realistic and effective environment for the development and evaluation of intelligent anomaly-detection systems in industrial CPS. Moreover, the proposed testbed can be considered a solid foundation for future research in predictive maintenance, process optimization, and industrial cybersecurity. Keywords: Testbed, Anomaly detection, Cybersecurity, Digital Twin, Cyber-physical system, Programmable Logic Controller (PLC), Machine Learning, Industrial control.