Intrusion Detection System for Electric Vehicle Charging Station

The security of the infrastructure used to charge electric vehicles (EVs) has become increasingly important due to their growing adoption. In order to address this important problem, the research suggests creating an intrusion detection system (IDS) that makes use of machine Learning and deep learning algorithms. A comprehensive evaluation of several models, in Machine Learning including Naive Bayes, Support Vector Machines (SVM), and Deep learning including Long Short Term Memory (LSTM) networks and deep Neural Networks (DNN), carried out using the CICIDS 2017 dataset. To guarantee a thorough comprehension of their performance, the suggested models are contrasted using performance metrics. The results of this study should produce a unique and reliable intrusion detection system, which would greatly improve the cybersecurity of electric vehicle infrastructure. This will also help in analyzing how well deep learning models and traditional machine learning methods perform in terms of intrusion detection.