Attack-Resilient Event-Triggered Control of Vehicle Speed Tracking System With DoS Attacks: Experimental Results
Bibliographic record
Abstract
This paper presents an attack-resilient event-triggered mechanism (AETM) control for the longitudinal speed tracking of connected vehicles, addressing the issue of communication data saturation in the in-vehicle controller area network (CAN) caused by denial of service (DoS) attacks. In current studies, compensation data is transmitted after DoS attacks based on known attack statistics. Additionally, compensating data during DoS attacks may lead to significant performance loss in systems and CAN bus-off. To address this, an AETM is proposed to alleviate CAN data saturation during DoS attacks, at the cost of reductions in critical data. To accommodate infrequent critical data induced by the AETM, a new robust controller is designed aiming to minimize performance loss. The closed-loop discrete-time model is developed with attack-induced uncertainties and event-triggered instants which vary with DoS attacks. Then, the controller gain can be obtained by solving a series of linear matrix inequalities (LMIs) with MATLAB LMI tool box and particle swarm optimization (PSO) algorithm. Finally, hardware-in-the-loop (HiL) experiments demonstrate the effectiveness of the proposed method in mitigating CAN data saturation and improving vehicle safety under DoS attacks.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".