A Bootstrapped Volatile Key Management Scheme for Efficient and Secured Data Transmission in WSNs
Bibliographic record
Abstract
The recent era has witnessed several advances in Wireless Sensor Networks (WSNs), with few resources presents unprecedented challenges for secured data transmission in an ever-unsecured broadcast network. The situation is more challenging when adversaries are equipped with sophisticated resources having direct access to sensor nodes. Nevertheless, there exists several cryptographic techniques used for node authentication, authorization, data confidentiality, integrity, and other security related services. This paper contributes to novel network security strategies to prevent network compromise. The proposed strategies attempt to boost data integrity, reliability and secure data transmissions. The proposal identifies the strategically important nodes (SIN) in implementing network security measures using volatile symmetric-key management scheme (KMS).The scheme involves four phases: The first phase involves strategically important nodes (SIN) designation using Grey Wolf Optimization (GWO). Usually, hackers find it difficult to guess and access the keys that are selected on a random basis. To further increase the key protection, the scheme introduces a limited lifespan master key (LLMK). Secondly, to secure the KMS, this phase utilizes SHA for LLMK distribution, and paired node prediction. Next phase performs secret key generation with a gradient approach and the key sharing is performed using Supersingular Isogeny Diffie-Hellman algorithm. The last phase is responsible for the dynamic S-Box generation using the Blowfish algorithm. The generated S-Box are subsequently shuffled to enhance the cryptographic process offering immunity towards attacks.To evaluate the effectiveness of this approach, the proposed KMS is compared with relevant methods on various performance metrics. The results demonstrate that the proposed scheme achieves notable performance improvements over other methods.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".