Vehicular Network Security Against RF Jamming: An LSTM Detection System
Bibliographic record
Abstract
The evolution of Intelligent Transportation Systems has ushered in a new era, offering extensive safety and comfort services through Vehicular Networks (VNs). With connected vehicles generating and disseminating vast amounts of data, encompassing critical safety and traffic information, the vulner-ability of VNs to Radio Frequency (RF) jamming has emerged as a significant concern, specially for intrusion detection systems. The dynamic nature of high-mobility vehicles and densely populated areas, coupled with the presence of roadside units, poses challenges in distinguishing between malicious attackers and legitimate transmitters. In this paper, we introduce an intelligent strategy for the classification of RF jamming attacks in VNs where Long Short-Term Memory (LSTM) networks are applied to a well-curated dataset to construct a model capable of identifying potential attacks. Various parameters, including signal characteristics, temporal aspects, and relative speeds, are considered to comprehensively address the classification task. Our proposed model demonstrates outstanding performance across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Through iterative analyses, we illustrate the incremental improvements achieved by our model compared to existing methodologies in terms of effectively classifying RF jamming in vehicular networks.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".