EV charging station attack detection using machine learning

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dc.contributor.authorJanwiri, Kamran Athar
dc.contributor.supervisorGebali, Fayez
dc.date.accessioned2024-12-19T19:55:10Z
dc.date.available2024-12-19T19:55:10Z
dc.date.issued2024
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Engineering MEng
dc.description.abstractElectric 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.
dc.description.scholarlevelGraduate
dc.identifier.urihttps://hdl.handle.net/1828/20869
dc.language.isoen
dc.rightsAvailable to the World Wide Web
dc.subjectEV charging station attack detection using machine learning
dc.subjectEVSE
dc.subjectEV
dc.subjectelectric vehicle
dc.titleEV charging station attack detection using machine learning
dc.typeproject

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