Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement
Bibliographic record
Abstract
Security provisioning has become the most important design consideration for large-scale Internet of Things (IoT) systems due to their critical roles in supporting diverse vertical applications by connecting heterogenous devices, machines, and industry processes. Conventional authentication and authorization schemes are insufficient to overcome the emerging IoT security challenges due to their reliance on both static digital mechanisms and computational complexity for improving security levels. Furthermore, the isolated security designs for different layers and link segments while ignoring the overall protection leads to cascaded security risks as well as growing communication latency and overhead. In this article, we envision new artificial intelligence (AI)-enabled security provisioning approaches to overcome these issues while achieving fast authentication and progressive authorization. To be more specific, a lightweight intelligent authentication approach is developed by exploring machine learning at the base station to identify the prearranged access time sequences or frequency bands or codes used in IoT devices. Then we propose a holistic authentication and authorization approach, where online machine learning and trust management are adopted for achieving adaptive access control. These new AI-enabled approaches establish the connections between transceivers quickly and enhance security progressively so that communication latency can be reduced and security risks are well controlled in large-scale IoT systems. Finally, we outline several areas for AI-enabled security provisioning for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".