DTHA: A Digital Twin-Assisted Handover Authentication Scheme for 5G and Beyond
Bibliographic record
Abstract
With the rapid development and extensive deployment of the fifth-generation wireless system (5G), it has achieved ubiquitous high-speed connectivity and improved overall communication performance. Additionally, as one of the promising technologies for integration beyond 5G, digital twin in cyberspace can interact with the core network, transmit essential information, and further enhance the wireless communication quality of the corresponding mobile device (MD). However, the utilization of millimeter-wave, terahertz band, and ultra-dense network technologies presents urgent challenges for MD in 5G and beyond, particularly in terms of frequent handover authentication with target base stations during faster mobility, which can cause connection interruption and incur malicious attacks. To address such challenges in 5G and beyond, in this paper, we propose a secure and efficient handover authentication scheme by utilizing digital twin. Acting as an intelligent intermediate, the authorized digital twin can handle computations and assist the corresponding MD in performing secure mutual authentication and key negotiation in advance before attaching the target base stations in both intra-domain and inter-domain scenarios. In addition, we provide the formal verification based on BAN logic, RoR model, and ProVerif, and informal analysis to demonstrate that the proposed scheme can offer diverse security functionality. Performance evaluation shows that the proposed scheme outperforms most related schemes in terms of signaling, computation, and communication overheads.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".