A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks
Bibliographic record
Abstract
The Vehicle-to-Grid (V2G) networks are a part of the Smart Grid networks. Their primary goal is to recharge electric vehicles. These networks, as with any computer system, are facing cyber attacks. For example, during a charge or recharge process, V2G networks can be vulnerable to attacks such as Man-in-the-Middle (MitM), Denial of Service (DoS), identity theft, and rebound attacks. It is therefore up to us to offer innovative solutions in order to reduce threats as much as possible. In this paper, a model based on copulas to detect intrusion cases in V2G networks is proposed. To achieve this model, a database is generated first from three scenarios using tools including MiniV2G, Wireshark, and CICflowMeter. Then, significant variables are selected using Principal Component Analysis (PCA). The classification algorithm is based on the notion of copulas constructed under the software R. From the obtained results, it emerges that the created model has a very high prediction rate of attacks in the aforementioned network.
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.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".