Today, we are transitioning towards the Smart Grid era. As this comes with a lot of advantages such as the inclusion of Sustainable Energy Technologies, Electric Vehicles, Micro Grids, Smart Metering, etc., areas such as cyber security should play a major role. Therefore, we found an opportunity area in the Electric Vehicle Charging Station infrastructure. Today, Electric Vehicle Charge Point Operators, do not have clear visibility of the traffic that travels across their back-system and the charge points. The latter implies that fraudulent data transaction or cyber attacks can be performed without the operators noticing the problem.
With the help of Machine Learning, we want to develop and Intrusion Detection System (IDS) that will identify any anomaly in the traffic among the back-end system of the charge point operator and the Electric Vehicle Charging Station. The IDS will notify the operator in case any deviation is detected. The algorithm should be able to classify the alert according to its type (fraud, change in voltage value, etc.) together with an indication of the risk level.