The increasing digitalization of energy infrastructures, particularly electric vehicle (EV) charging systems, has expanded their vulnerability to cyber threats. This paper presents a modular AI-driven platform for detecting attacks on EV charging infrastructures. The platform combines real-time data collection tools (Tshark, Nozomi Guardian, SNMP/Syslog, Elasticsearch Logstash and Kibana (ELK)) with Long Short-Term Memory (LSTM) Autoencoder-based anomaly detection. Data were gathered from a charging facility at RSE with twelve OCPP-J v1.6 charging stations, including normal and simulated Denial of Service (DoS) scenarios. The model, trained on multivariate time-series traffic data, achieved 97.1% Accuracy and 98.6% Recall, ensuring robust anomaly detection and minimizing false negatives. Although Precision was lower (52%) due to traffic variability, the system effectively detected both cyber-induced and operational anomalies, such as station disconnections. This work demonstrates the value of integrating deep learning with real-time monitoring to enhance the resilience of smart energy systems. Future developments will focus on improving precision, expanding protocol coverage, and addressing advanced threats such as data injection and Man-in-the-Middle attacks.