Cyber-Attack on P2P Energy Transaction Between Connected Electric Vehicles: A False Data Injection Detection Based Machine Learning Model
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
When cybersecurity is neglected, any network system loses its efficiency, reliability, and resilience. With the huge integration of the Information, Communication and Technology capabilities, the Connected Electric Vehicle (CEV) as a transportation form in cities is becoming more and more efficient and able to reply to citizen and environmental expectations which improve the quality of citizens’ life. However, this CEV technological improvement increases the CEV vulnerabilities to cyber-attacks resulting to serious risks for citizens. Thus, they can intensify their negative impact on societies and cause unexpected physical damage and economic losses. This paper targets the cybersecurity issues for CEVs in parking lots where a peer-to-peer(P2P) energy transaction system based on blockchain, and smart contract scheme is launched. A False Data Injection Attack (FDIA) on the electricity price and power signal is proposed and a Machine Learning/SVM classification protocol is used to detect and extract the right values. Simulation results are conducted to prove the effectiveness of this proposed model.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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