Performance Evaluation of Machine Learning-Based Cyber Attack Detection in Electric Vehicles Charging Stations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electric Vehicles (EV) chargers rely on resource-constrained embedded hardware to execute critical charging operations. However, conventional security solutions may not adequately meet the needs of these devices. Increasingly, machine learning techniques are being leveraged to detect cyber attacks during electric vehicle charging. This study aims to evaluate various base machine learning methods and conduct binary and multi-class classification experiments to enhance security and operational efficiency in EV charging stations. The experiments utilize the CICEVSE2024 dataset, curated by the Canadian Institute for Cybersecurity at the University of New Brunswick, designed specifically for anomaly detection and establishing behavioral patterns in EV charging stations. The analysis highlights nuances in performance across different machine learning classifiers. For instance, Random Forest achieved 95.07% accuracy in binary classification by constructing robust decision trees. Ensemble methods such as CatBoost and LightGBM further improved binary classification to 95.37% and 95.41%, respectively through gradient boosting techniques. In multi-class attack classification, ensemble methods demonstrated superior performance, with the Stacking Ensemble achieving 91.1% accuracy by combining multiple models, and Voting Ensemble achieving 90.7%. Notably, among homogeneous base classifiers, Extra Trees and HistGradient Boosting were particularly effective, achieving 90.2% and 89.8% accuracy respectively in multi-class classification tasks. These findings underscore the efficacy of machine learning in enhancing cybersecurity measures for EV charging infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it