Clustering Algorithms for Anomaly Detection in EVCS Infrastructure using OCPP
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
Anomaly detection in Open Charge Point Protocol (OCPP) is key to securing the infrastructure of electric vehicle charging stations (EVCS), which are increasingly vulnerable to cyber threats. Anomaly detection enables cyber-attack detection, and supervised learning is commonly used. However, its dependence on labeled datasets, often sparse and unbalanced, limits its detection ability on advanced threats. This study highlights the limitations of supervised learning techniques and explores clustering in semi-supervised and unsupervised learning techniques. To our knowledge, no previous work focuses on anomaly detection using clusters for semi-supervised and unsupervised learning applied to the CICEVSE2024 dataset. Our key contribution enhances OCPP security, enabling more robust anomaly detection in EVCS infrastructure and the broader smart grid ecosystem.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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