Toward Zero-Trust 6GC: A Software Defined Perimeter Approach with Dynamic Moving Target Defense Mechanism
Bibliographic record
Abstract
The upcoming Sixth Generation (6G) network is projected to grapple with a range of security concerns, encompassing access control, authentication, secure connections among 6G Core (6GC) entities, and trustworthiness. Classical Virtual Private Networks (VPNs), extensively deployed in Evolved Packet Core (EPC) network infrastructure, are notoriously susceptible to a variety of attacks, including man-in-the-middle incursions, Domain Name System (DNS) hijacking, Denial of Service (DoS) attacks, port scanning, and persistent unauthorized access attempts. This paper introduces the concept of Software Defined Perimeter (SDP) as an innovative solution, providing an alternative to VPNs with the goal of fostering a secure zero-trust milieu within the 6G Core networks. We capitalize on the SDP controller-based authentication and authorization mechanisms to secure the EPC network's control and data plane functions, conceiving an architecture that is expansible to the 6G network. Further, we augment the SDP zero-trust capabilities via the incorporation of a dynamic component, the Moving Target Defense (MTD). This enhances the network's resilience against attacks targeting traditionally static network environments established via VPNs. Following rigorous testbed analysis, our proposed framework manifests superior resilience against DoS and port scanning attacks when jux-taposed with traditional VPN methodologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".