DCSM: A Distributed and Collaborative Security Monitoring Module to Detect Cyber-Attacks in the EV Ecosystem
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) 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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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