A Blockchain-Based Authentication and Key Agreement (AKA) Protocol for 5G Networks
Bibliographic record
Abstract
Subscriber authentication is a primitive operation in mobile networks required by each operator prior to offering any service to end users. In this paper, we propose a novel blockchain-based Authentication and Key Agreement (AKA) protocol for roaming services in 5G networks. Each Home Network (HN) creates its own smart contract and publishes its address to inform other operators who want to offer roaming services to HN subscribers. All subsequent communication between the HN and Serving Network (SN) is done by calling the function of this smart contract. The proposed protocol eliminates the need for a secure channel between the HN and SN, which is a primary requirement of current 5G AKA protocols. In practice, a secure channel requires the HN and SN to establish a secure session before running the AKA protocol. Further, the proposed protocol leverages the benefits of blockchain, such as auditable log, decentralized architecture, and the prevention of Denial of Service (DoS) attacks. Furthermore, we provide a security proof of the protocol through formal verification using ProVerif. The results show that our scheme tends to preserve user privacy and at the same time provides mutual authentication of the participants. Finally, our evaluation of the Ethereum blockchain shows that the protocol is efficient in terms of both transaction and execution costs.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".