A New Approach to Improving the Security of the 5G-AKA Using Crystals-Kyber Post-Quantum Technologies and ASCON Algorithm
Bibliographic record
Abstract
The 5G-AKA protocol includes several vulnerabilities related to security and privacy.This paper proposes improving the standard 5G-AKA protocol by enhancing security and privacy, such as optimal forward secrecy, resilience against linkability attacks, and protection against malicious SN networks.The proposed protocol, called KyberPQ-AKA, includes two stages of development.In the first stage, a Crystals-Kyber KEM-based method creates keys and safely exchanges them within the AKA protocol environment.In the second stage, the lightweight encryption algorithm ASCON replaces traditional encryption in the protocol to work on devices with limited resources.Moreover, key encapsulation (KEM) mechanisms improve the protection of user identity and complete privacy.KyberPQ-AKA makes it easier to adapt to a quantum-secure environment and provides additional security and authentication benefits by switching from the AES encryption algorithm to the lightweight ASCON algorithm.KEM Crystals-Kyber postquantum, a criterion NIST recently chose, and KEM candidates for the fourth round after quantum from NIST used in the suggested protocol.The results on connection and calculation costs indicate that the KyberPQ-AKA protocol is practical and superior to standard 5G-AKA.We also proved the security of KyberPQ-5G using the ProVerif tool and by applying the protocol using Mininet with RYU Controller to test the protocol.The results proved this in comparison with the standard 5G-AKA protocol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".