SDN-Based Secure and Privacy-Preserving Scheme for Vehicular Networks: A 5G Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it