Beyond the Attack: Strategies for Attack Endgame Mitigation in VANETs
Bibliographic record
Abstract
In Vehicular Ad Hoc Networks (VANETs), neutralizing attack effects is essential for safeguarding human safety and maintaining network efficacy. This paper proposes new strategies to preempt and neutralize the attack endgame. Our strategies specifically address Fake Reporting Attacks, notorious for creating deceptive messages that lead to hazardous and life threatening situations. The significant frequency and severity of these attacks, culminating in road hazards, underscore the urgency for a concentrated research effort in this field. Our research adopts a dual-faceted approach, comprising both preventative measures for attack endgames and subsequent counteractive strategies. Utilizing the VeReMi for Attack Prediction (VeReMiAP) dataset and leveraging advancements in attack effect prediction, our research introduces a dual-phase approach to mitigate attack endgames. The first phase, endgame prevention, is achieved by deploying advanced Recurrent Neural Network (RNN) models that forecast the potential impact of harmful messages, thereby enabling early intervention. The second phase, endgame countermeasure, employs Geofencing, which uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for strategic message control and Dynamic Blacklist Management (DBM) based on Reputation System-Based Lightweight Message Authentication (RSMA) to exclude malign vehicles. Our findings indicate a reduction in hazardous events by over 40% and a threefold increase in vehicular speed recovery within the network, substantiating the efficacy of our proposed solutions in bolstering VANET security.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".