Ensemble-based Intrusion Detection System for Electric Vehicles Charging Stations using Machine Learning
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Bibliographic record
Abstract
Traditional Vehicles have an adverse effect on the environment. Therefore, the current technological shift is constantly seeking an alternative to replace traditional vehicles fueled by fossil fuels, and Electric vehicles are, so far, the best alternative. The adoption of Electric Vehicles (EVs) is growing rapidly due to their eco-friendly benefits and technological advancements. This growth, however, brings a significantly larger attack surface due to increased interconnectivity between electric vehicles, charging stations and the smart grid system. To prevent such types of attacks, we need a robust system to detect them beforehand and prevent the system from being compromised. Although some prior work has been conducted in this area, their approaches did not incorporate deep learning algorithms, nor did they evaluate model performance under noisy data conditions. Therefore, we proposed a novel ensemble-based intrusion detection system (IDS) to detect these attacks in Electric Vehicle Charging Stations (EVCS). We implement different Machine learning algorithms such as k-nearest neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT). Moreover, as different types of malwares often exhibit distinct structural characteristics when visualized as images, we also use Convolutional Neural Networks (CNNs) to detect such attacks and malware. We are focusing on detecting attacks in Electric vehicle charging stations by analyzing the network traffic. For this, we utilize the latest labelled dataset, the Canadian Institute of Cybersecurity EV Charger Attack Dataset 2024 (CICEVSE2024), which is a multidimensional dataset containing both benign and attack data. We then evaluate & compare the performance of these algorithm in detecting the network traffic attacks in Electric Vehicle Charging Stations (EVCS). Our proposed model employs an ensemble voting strategy to combine the predictions from different classifiers, thereby improving the system's robustness and accuracy, and achieves an accuracy of 99.5% in detecting cyberattacks. With the addition of small noise to the dataset, a few individual classifiers perform poorly; however, the ensemble model still maintains an accuracy of 99.2%.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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