Electric Vehicle charging infrastructure is crucial to the sustainable energy transition but it is also susceptible to cyberattacks. Protection against possible cyberattacks, such as Distributed Denial of Service (DDoS) assaults, has become crucial with the growth of electric vehicle supply equipment (EVSE) networks. The extensive Electric Vehicle Supply Equipment (EVSE) dataset, which includes high-performance computing (HPC) statistics, system performance indicators, and network traffic data, is used to analyze the proposed approach, and to create a strong anomaly detection framework by utilising deep learning techniques, to analyse host-based data, such as kernel eventlogs and HPC data. LSTM based model is used for sequence-based learning, analysing high-granularity event sequences to identify anomalous patterns, and putting in place a preprocessing pipeline that guarantees dependable data input for the model are some of the main contributions.