DCSM: A Distributed and Collaborative Security Monitoring Module to Detect Cyber-Attacks in the EV Ecosystem
Bibliographic record
Abstract
Electric Vehicles (EV) have become increasingly popular due to their sustainable benefits, but the complexity of the EV ecosystem, comprising components such as the EV Charging Stations (CS), EV Charging Station Management Systems (CSMS), and the broader smart grid, poses significant security challenges. Previous solutions have relied on isolated anomaly detection mechanisms, limiting their ability to detect attacks across the entire EV ecosystem. This paper presents DCSM, a distributed and collaborative security monitoring module that is integrated with a real-time monitoring platform that combines and correlates data from different EV ecosystem components. It detects anomalies locally in each component and compiles reports at the Utility for centralized decision-making. In addition to EV components, we also consider a new monitoring component, the Smart Meters (SM) connected to the CSs. By offering a distinct vantage point, the SM enhances the platform's robustness, even if other data sources are compromised. To validate our approach, we analyze multiple attack scenarios targeting the ecosystem and assess the performance of the detection module. Our results highlight the module's effectiveness in protecting public EV charging infrastructure from various cyber threats.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".