Analysis of Data-Driven Detection and Localization of Cyberattacks on Faulty Electric Vehicle Platoons
Bibliographic record
Abstract
Connected autonomous vehicle (CAV) platooning enhances the perception and decision-making abilities of autonomous vehicles to improve traffic efficiency. However, intervehicle communications introduce vulnerabilities to cyberattacks, which can lead to unsafe conditions. To this end, data-driven CAV intrusion detection and classification have been the subject of recent work. Concurrently, adoption of electric vehicles (EVs) is growing to meet pressing environmental targets, and the dynamics of EVs offer many advantages that qualify them over conventional vehicles for platooning applications. We identify a need to examine the cyberphysical security of CAEVs, where EV faults occur simultaneously with cyberattacks and the modified dynamics of a faulty vehicle can affect the performance of an attack classifier. In this paper, we explicitly model EV motor dynamics and simulate platooning behavior in healthy, under-attack, under-fault, and simultaneous attack and fault scenarios. We propose the use of an attention-based attack classifier and compare its performance to traditional machine learning architectures in simultaneous attack and fault scenarios. Our results, based on platoon velocity data, demonstrate that the attention-based classifier outperforms other methods in most scenarios except for EVs operating with low battery state-of-charge. We highlight the need for further research on attack classifiers robust to physical fault scenarios.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".