Anti-Jamming V2V Communication in an Integrated UAV-CAV Network with Hybrid Attackers
Bibliographic record
Abstract
Connected and autonomous vehicles (CAVs) and unmanned aerial vehicles (UAVs) are viewed as revolutionary technologies in the era of Internet of Things (IoT). However, both CAV and UAV can be exploited by potential adversaries and pose serious threats to the intelligent transportation system (ITS), such as damaging the vehicle-to-vehicle (V2V) communication. In this paper, we investigate the anti-jamming V2V communication in an integrated UAV-CAV network with hybrid attackers, which consist of a malicious CAV with intelligent jamming capability and a malicious UAV without. To solve this problem, we propose to use a unique research tool named cognitive dynamic system (CDS), and apply its function of cognitive risk control (CRC) to develop an effective countermeasure. In each perception-action cycle (PAC), the power control will always be performed by a legitimate transmitting vehicle; meanwhile, the process of channel selection only takes place if the risk level is evaluated as high after completing the power control. This kind of design that involves task-switching is inspired by the predictive-adaptation feature of the human brain. Simulation results have shown that the proposed method based on CRC is able to defend hybrid attackers effectively under various settings.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".