A Hybrid Deep Learning Approach for DDoS Attacks Detection in EV Charging Stations
Bibliographic record
Abstract
The rapid deployment of Electric Vehicle (EV) charging station has introduced new cybersecurity challenges, particularly a vulnerability to distributed Denial-of-Service (DDoS) attacks that can compromise the system’s availability and reliability. This paper proposes a hybrid deep learning-based intrusion detection model (denoted LSTM-GRU-MLP) for detecting DDoS attacks in EV charging stations. The proposed model leverages the temporal sequence learning capabilities of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, combined with the classification strength of a Multi-Layer Perceptron (MLP), to capture the sequential and high-dimensional representations from time-series operational data. A comprehensive evaluation is conducted using the CICEV2023 DDoS attack dataset, showing that it achieves $94 \%, 94 \%, 99 \%$ and 97% in terms of accuracy, precision, recall and F1-score, respectively, Compared to our proposed benchmark model Deep Convolutional Neural Network (DCNN), which achieved 88% accuracy, 90% precision, 95% recall, and 92% F1-score. Index Terms-EV Charging Station, Hybrid Deep approach, Long Short-Term Memory, Gated Recurrent Unit Multi-Layer Perceptron(MLP),DDoS attacks and detection, CICEV 2023 datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".