Phishing and Sybil Enhanced Behavior Processing and Footprint Algorithms in Vehicular Ad Hoc Network
Bibliographic record
Abstract
Communication between vehicles is the core of VANETs, which are built on notions of mobile ad hoc networks (MANETs). Each car on the network is now seen as a mobile network node. All participating cars become wireless routers or nodes, depending on your viewpoint. VANET creates a huge network with a long-range by connecting all cars within range to a fixed unit. VANET assists with traffic regulation, communication between vehicles and the sharing of road data. There is a possibility that the VANET network could be compromised by identity and information concerns, resulting in data delays or theft. Attacks like Sybil and phishing are possible because of this network's weakness. Due to the two recent strikes, infrastructure and human lives may be in jeopardy. There are two novel algorithms developed by the authors to tackle Sybil and Phishing assaults on VANET networks: Phishing and Sybil Enhanced Behavior Processing and Footprints (P&SEBP&F). Originally known as the Phishing and Sybil Enhanced Behavior Processing and Footprint, P&STL&T was renamed P&STL&T. Phishing and Sybil were used as well as varied attacker-to-victim node ratios to test the effectiveness of the new tactics for assessing effectiveness. Compared to previous study and the work of the other authors cited, there were approximately 30% fewer attackers detected during the research.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".