Janwiri, Kamran Athar2024-12-192024-12-192024https://hdl.handle.net/1828/20869Electric Vehicle (EV) charging stations are very important for supporting the adoption of EVs, but they are at risk of cyberattacks. This project looks at how Machine Learning (ML) can help to detect these attacks using the CICEVSE2024 dataset, which has data about normal operations and attack scenarios. ML models like Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machines (SVM), Logistic Regression (LR), Naive Bayes (NB), and k-Nearest Neighbors (KNN) were tested. Principal Component Analysis (PCA) applied to simplify the dataset by minimizing the features, and SMOTE (Synthetic Minority Oversampling Technique) was used to balance the dataset. Models were evaluated with 21, 15, 10, and 5 features to find the best accuracy and speed. RF shows the best accuracy whereas KNN was the fastest.enAvailable to the World Wide WebEV charging station attack detection using machine learningEVSEEVelectric vehicleproject