Achieve Secure Handover Session Key Management via Mobile Relay in LTE-Advanced Networks
Bibliographic record
Abstract
Internet of Things is expanding the network by integrating huge amount of surrounding objects which requires the secure and reliable transmission of the high volume data generation, and the mobile relay technique is one of the efficient ways to meet the on-board data explosion in LTE-Advanced (LTE-A) networks. However, the practice of the mobile relay will pose potential threats to the information security during the handover process. Therefore, to address this challenge, in this paper, we propose a secure handover session key management scheme via mobile relay in LTE-A networks. Specifically, in the proposed scheme, to achieve forward and backward key separations, the session key shared between the on-board user equipment (UE) and the connected donor evolved node B (DeNB) is first generated by the on-board UE and then securely distributed to the DeNB. Furthermore, to reduce the communication overhead and the computational complexity, a novel proxy re-encryption technique is employed, where the session keys initially encrypted with the public key of the mobility management entity (MME) will be re-encrypted by a mobile relay node (MRN), so that other DeNBs can later decrypt the session keys with their own private keys while without the direct involvement of the MME. Detailed security analysis shows that the proposed scheme can successfully establish session keys between the on-board UEs and their connected DeNB, achieving backward and forward key separations, and resisting against the collusion between the MRN and the DeNB as the same time. In addition, performance evaluations via extensive simulations are carried out to demonstrate the efficiency and effectiveness of the proposed scheme.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".