SDN-Based Secure and Privacy-Preserving Scheme for Vehicular Networks: A 5G Perspective
Bibliographic record
Abstract
The ever-increasing demands of vehicular networks pose significant challenges such as availability, computation complexity, security, trust, authentication, etc. This becomes even more complicated for high-speed moving vehicles. As a result, increasing the capacity of these networks has been attracting considerable awareness. In this regard, the next generation of cellular networks, 5G, is expected to be a promising solution enabling high data rates, capacity, and quality of service as well as low latency communications. However, 5G networks still face challenges in providing ubiquitous and reliable connections among high-speed vehicles. Thus, to overcome the gaps in the existing solutions, we propose a software defined network (SDN)-based consolidated framework providing end-to-end security and privacy in 5G enabled vehicular networks. The framework simplifies network management through SDN, while achieving optimized network communications. It operates in two phases: first, an elliptic curve cryptographic based authentication protocol is proposed to mutually authenticate the cluster heads and certificate authority in SDN-based vehicular setups, and, second, an intrusion detection module supported by tensor based dimensionality reduction is designed to reduce the computational complexity and identify the potential intrusions in the network. In order to assess the performance of the proposed framework, an extensive evaluation is performed on three simulators; NS3, SUMO, and SPAN. To harness the potential benefits of the proposed model, the first module, is evaluated on the basis of security features, whereas the second module is evaluated, and compared with the existing state-of-the-art models, on the basis of detection rate, false positive rate, accuracy, detection time, and communication overhead. The simulation results indicate the superiority of the proposed framework as compared to the existing models.
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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) | 0.000 | 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 | 0.001 |
| 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".