Cloud-based Sybil Attack Detection Scheme for Connected Vehicles
Bibliographic record
Abstract
Automated and connected vehicle technologies are among the most heavily researched automotive technologies. As a part of an Intelligent Transportation System (ITS), connected vehicles provide useful information to drivers and the infrastructure to help make safer and more informed decisions. However, vehicle connectivity has made the ITS more vulnerable to security attacks that can endanger vehicle's security as well as driver's safety. Sybil attack is a very common attack, considered dangerous in a distributed network with no centralized authority. When launched against connected vehicles, it consists of controlling a set of vehicles with forged or fake identities to try to alter the measurements and data collected by the ITS, leading to sub-optimal decisions. In this paper, we provide a cloud-based detection scheme for connected vehicles against such an attack. Contrary to the previous distributed solutions in the literature, this paper presents a cloud-based solution that integrates a cloud-based authorization unit to authenticate legitimate nodes using symmetric cryptography and real-time location tracking. As a centralized authentication system, cloud computing is more reliable and secure in managing the vehicle as a device than any other infrastructure in the vehicular network and can provide real-time visibility. A trust evaluation approach is also integrated into the scheme to drive the decisions of the vehicles concerning potential collaborations. The performed experiment and security analysis show the efficacy of our proposed cloud-based solution in terms of detection rate, complexity and system requirements.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".