Detecting Selective Modification in Vehicular Edge Computing
Bibliographic record
Abstract
Mobile Edge Computing can be used to realize the low latency requirements of vehicular networks. However, by compromising the road side units (RSUs), an adversary can introduce an extra delay leading to various problems such as the wastage of edge computing resources and disruption of navigational and safety functions. The compromised RSU can for instance deliberately corrupt the PHY layer payload of the packets to be transmitted to the vehicles. With this simple attack, the adversary would increase latency and through that effect, create serious disruptions. Such an attack can affect many critical delay sensitive applications such as collision avoidance. To detect the presence of such an adversary, we propose a trust based detection system in this paper. Each vehicle transmits a feedback packet about every RSU it has interacted with to a central trusted server. Using the feedback obtained from multiple vehicles, at regular intervals, an aggregated trust value for each RSU in the network is obtained and is compared with a threshold to classify the RSU as authentic or malicious. We also present a mechanism to detect the presence of malicious vehicles reporting false feedback in the network. Simulation results presented demonstrate the effectiveness of the proposed detection mechanism and the impact of the choice of adversary parameters on the detection system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".