Leveraging Group Secret Sharing Technology for FD-RAN: A Lightweight AKA Mechanism
Bibliographic record
Abstract
With rapid advances in communication technology, a new access architecture of fully decoupled radio access network (FD-RAN) has been proposed. FD-RAN completely decouples the base station (BS) into uplink data base station (UBS), downlink data base station (DBS), and control base station (CBS). Different BSs handle the uplink and downlink data of the user plane, as well as control signaling, and facilitate communication needs through multi-BS cooperation. To ensure the security of multi-BS cooperation and user access, it becomes imperative to conduct key negotiations among multiple parties. However, as the number of simultaneously accessed BSs increases, the existing access security mechanism imposes excessive overhead in FD-RAN, compromising both access security and efficiency. Additionally, it becomes susceptible to distributed denial of service (DDoS) attacks launched by potential attackers. This paper introduces a lightweight authentication and key agreement (AKA) protocol based on secret value ($m_{i},\ n_{i}$) sharing technology to negotiate multi-BS group communication keys, which ensures access security in FD-RAN. By leveraging interpolation polynomial and multi-party key negotiation, the proposed protocol achieves efficient and cost-effective key negotiation on both the user and BS sides, which mitigates the risk of man-in-the-middle (MitM) and DDoS attacks. Security analysis and further evaluation show that the proposed scheme can resist various known attacks, and guarantee the computational and communication efficiency of key negotiation within the FD-RAN context.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".