Security Framework for Internet-of-Things-Based Software-Defined Networks Using Blockchain
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
Presently, trillions of Internet of Things (IoT) devices are in use, with many more projected to join IoT networks in the future. These IoT devices create a massive volume of data, which cannot be transmitted over the network without proper security and privacy. Furthermore, as the amount of information and variety of interconnected devices grows, problems, including excessive response time, bandwidth constraints, and scalability, emerge in proper network design. To solve the constraints of today’s smart cities for next-generation networks, an effective, secure, and scalable distributed framework must be designed bringing computing and storage resources nearer to endpoints. In this article, combining the strengths of software-defined networks (SDNs) and blockchain technology, an innovative adaptable network infrastructure for smart cities is developed. The network is divided into different domains in which SDN will detect potential attacks and transmit the secured data to the blockchain. Our in-depth experimental analysis on performance evaluation show that the proposed framework achieves 12.75% improvement over baseline methodologies.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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