Towards a Secure and Scalable Access Control System Using Blockchain
Bibliographic record
Abstract
Access control, both physical and virtual, has always been a crucial aspect in maintaining the security of corporate information systems. Recently, several solutions have been proposed to address physical and virtual access control, including the use of electronic badges that can be costly to produce and easily misplaced. Additionally, numerous, sometimes expensive, cloud-based solutions have also been adopted. However, these solutions are often provided by third-party organizations, which require entities to place trust in these providers, a risk that is unacceptable for industries such as the military and banking. To solve this issue, we propose a novel Blockchain-based solution to establish a scalable and secure system for managing access controls. Blockchain offers a secure, decentralized, and most importantly, immutable alternative that eliminates the need for trust in third-party providers. We have implemented, tested, and deployed our Blockchain-based access control architecture on the Avalanche official network. The results demonstrate that this solution offers strong security, flexibility, efficiency, and cost-effectiveness, making it a promising approach to mitigate Distributed Denial of Service (DDoS) attacks in the Internet of Things (IoT). Our deployment on the Avalanche network confirms the feasibility and robustness of our approach in a real-world setting.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".