EAP-ZKP: A Zero-Knowledge Proof based Authentication Protocol to Prevent DDoS Attacks at the Edge in Beyond 5G
Bibliographic record
Abstract
5G has Introduced the primary and secondary authentication procedures to authenticate the user equipment requesting access to mobile network operators (MNOs) and service providers (SPs) data networks, respectively. However, the possibility of running distributed denial of service (DDoS) attacks on the MNO 5G core network (CN) and the SPs data networks still remains. In this paper, we introduce a zero- knowledge proof (ZKP) authentication algorithm called Partial- ID ZKP that authenticates users without revealing their service credentials. We show that Partial-ID ZKP has completeness and soundness properties. Based on Partial-ID ZKP, we then propose an extensible authentication protocol called EAP-ZKP that can be used in primary and secondary authentications to mitigate DDoS attacks at the CN edge. Finally, as a proof of concept, we implement EAP-ZKP in the 5G authentication procedure. Using the 5G simulators free5GC and gnbsim, we show that EAP-ZKP significantly reduces the authentication time for fake authentication attempts during DDoS attacks. Results also demonstrate that EAP-ZKP is able to recognize DDoS attack authentication attempts in about 10 msec. Interestingly, for the legitimate authentication attempts, the average authentication time slightly increases from 3.05 sec in current 5G authentication protocols to 3.06 sec in EAP-ZKP. This indicates that EAP-ZKP is promising for Beyond 5G.
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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 | 0.004 |
| 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 | 0.004 |
| Open science | 0.002 | 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".