LACS: A Lightweight Label-Based Access Control Scheme in IoT-Based 5G Caching Context
Bibliographic record
Abstract
Due to massive mobile terminal devices and ubiquitous communication, the Internet of things (IoT) has become an inevitable trend. Given that the fifth generation (5G) wireless networks expects to drive the proliferation of the IoT and may extend the access functions and systems of the IoT, it makes the IoT a vitally important part in future 5G wireless networks. Simultaneously, the limit of the bandwidth and power of the 5G would adversely affect the widespread promotion of the IoT. However, wireless caching techniques could remarkably resolve this issue. Recently, using fog nodes to improve the capacity of caching has become a trend in caching system. However, node-based caching systems may suffer from malicious access and destruction. To protect caching from sabotage and to further ensure its reliability, we propose a new lightweight label-based access control scheme (LACS) that authenticates the authorized fog nodes to ensure protection. Specifically, the LACS can authenticate the fog nodes by verifying the integrity of the shared files that are embedded label values, and only the authenticated fog nodes can access the caching service. The analysis shows that the proposed scheme is verifiable (the malicious fog node cannot cheat the caching server to pretend to be a legal node) and efficient in both computation and verification. Moreover, simulation experiments show that the LACS can reach the millisecond-level verification and it has a good accuracy.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".