Detection, Identification, and Mitigation of False Data Injection Attacks in Vehicle Platooning
Bibliographic record
Abstract
Vehicle platooning has gained significant attention due to its potential to enhance road safety, fuel efficiency, and traffic flow. However, the reliance on interconnected communication technology in platooning necessitates robust cybersecurity measures. This paper introduces frameworks for detecting and identifying cyber-attacks, specifically False Data Injection Attacks (FDIAs), aimed at securing vehicle platoons. To achieve this objective, a state-space model is developed, capable of accommodating any information flow topology and any number of vehicles within the platoon. To estimate the internal states of each vehicle, an Unknown Input Observer (UIO) is proposed. The detection of attacks on each vehicle is accomplished by employing a dedicated Detection UIO designed to detect FDIA on the respective vehicle. Furthermore, an Identification UIO is designed to identify compromised parameters of the attacked vehicle and mitigate the attacks by replacing the compromised parameters with their estimated authentic values. The effectiveness of the proposed approach is demonstrated through MATLAB simulations, encompassing various platooning configurations and attack scenarios. The simulation results highlight the accuracy of attack detection, particularly under stealthy attacks, and the successful identification of compromised vehicles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".