Publish Subscribe System Security Requirement: A Case Study for V2V Communication
Bibliographic record
Abstract
The Internet of Things (IoT) enables the linkage between the physical and digital domains, with wireless sensor networks (WSNs) playing a vital role in this procedure. The market is saturated with an abundance of IoT devices, a substantial proportion of which are designed for consumer use and have restricted power and memory capabilities. Our analysis found that there is very little research done on defining the security requirements of the IoT ecosystem. A crucial first step in the design process of a secure product entails meticulously scrutinizing and recording the precise security requirements. This paper focuses on defining security requirements for Vehicle-to-Vehicle (V2V) communication systems. The requirements are specified utilizing the Message Queuing Telemetry Transport for Sensor Network (MQTT-SN) communication protocol architecture, specifically tailored for use in sensor networks. The modified Security Requirement Engineering Process (SREP) and Security Quality Requirement Engineering (SQUARE) methodologies have been used in this paper for the case study. The security of the communication between the ClientApp and the road-side infrastructure is our main priority.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".