DeQoS Attack: Degrading Quality of Service in VANETs and Its Mitigation
Bibliographic record
Abstract
In this paper, we introduce a degradation-of-QoS (DeQoS) attack against vehicular ad hoc networks (VANETs). Through DeQoS, the attacker can relay the authentication exchanges between roadside units (RSUs) and faraway vehicles to establish connections but will not relay the service afterwards, which wastes the limited connection resources of RSUs. With enough number of dummy connections, RSUs' resources could run out such that they can no longer provide services for legitimate vehicles. Since the mobility of vehicles is highly related to the success probability of the attacker, we model the arrival and departure of vehicles into an M/M/N-queue system and show how the attacker can adaptively choose different attack strategies to perform the attack in distinct traffic environments. A series of simulations are conducted to verify the practicality of the attack using MATLAB. The experimental results demonstrate that the attacker can easily find exploitable vehicles and launch the DeQoS attack with an overwhelming probability (e.g., more than 0.98). As DeQoS exploits the weakness of lacking physical proximity authentication, only employing existing application-layer defense protocols in VANETs such as cryptography-based protocols cannot prevent this attack. Therefore, we design a new cross-layer relay-resistant authentication protocol by leveraging the distance-bounding technique. Security analysis is given to show that the defense mechanism can effectively mitigate DeQoS.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".