Enhanced Gated Sway Network and Hybrid Henon Encryption for Secured VANET Communication
Bibliographic record
Abstract
Vehicular Ad-hoc networks (VANETs) which is regarded to be a major component in the intelligent transportation systems, have the defined target of assuring safe delivery of information between the vehicles.These networks consist of several essential elements, such as dynamic changing nodes, scattered networks, sensors, road-side components (RSC) and self-organizing topologies.But these networks are more vulnerable to the contentious attacks, security breaches and data privacy problems persist as a crucial threat in spite of the recent advancement of VANET.To overcome this challenge, an effective and high secured framework is mandatorily demanded.Consequently, this research introduces a novel routing framework that integrates the attack detection and hybrid encryption units.The cluster head (CH) is determined utilising novel gated sway networks, which combine centrality-based feature extraction with a gated neural network to ensure trusted CH selection.This enhances resilience and improves interfacing throughout the data transmission process in the VANET framework.The hybrid encryption schemes contain sandwich Henon maps (SHM) coupled with the Advanced Encryption schemes (AES).This combination strives to strengthen the network's security and privacy The proposed protocols are analysed using SUMO-OMNET++ simulation environment.Nearly 2,50,000 data traces comprise of normal and attack data were simulated and attacks such as sybil and wormhole attacks are injected using python 3.19 programming.Simulation results from the performance assessment demonstrate that the proposed framework has produced the 96.5% detection accuracy, 96.0% precision, 95.7% recall, 96.4% specificity, 97.5% F1-Score and it is apparent that the proposed framework has exhibited the better performance over other existing algorithms.Additionally, National Institute of Standard Technique (NIST) suite was performed to verify the randomness of the encrypted bits utilising the recommended method.The test outcomes demonstrated that the suggested encryption approach has produced the high randomness features capable of protecting the sybil and gray hole attacks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".