Electrical systems are working close to their operational limits and the energy transitions are not even close to being fully implemented. In addition, the digitalization of energy systems is equipping electrical systems with sophisticated measurement equipment that has changed the way they communicate their data. Technological advances should not only concentrate on solving the problems of how to improve the system security with respect to the integration of renewables but also on preventing the cyber security of the system from being affected. In this project, the research groups of ZHAW and King Abdullah University of Science and Technology are joining forces and competencies to develop strategies based on data-driven and machine learning algorithms for anomaly detection of corrupted measurements from Wide-Area Monitoring System (WAMS) of power system utilities. The developed tools will be validated using Hardwarein-the-Loop (HIL) environment. The results of the project are intended to contribute to the transmission system utilities taking a step forward to reach the transition goals within the established timeframe.