Privacy-Preserving User Abnormal Behavior Detection in 5G Networks
Bibliographic record
Abstract
Network Data Analytics Function (NWDAF) plays a key role in autonomous network management and security using machine learning (ML) techniques. However, not all ML methods such as convolutional neural networks, deep language models can be deployed in the core network due to their high computational requirements. Besides, telcos may prefer handing over ML analytics to experienced third parties instead of implementing themselves. In this paper, we show that delay-tolerant tasks of NWDAF can be offloaded to public third party cloud providers while preserving user and data privacy by employing homomorphic encryption (HE). We focus on the problem of abnormal behaviour detection for a group of user equipment (UE) and train various DL models with plaintext data. We demonstrate that inferencing on encrypted data is as accurate and precise as plaintext inferencing and we quantify the performance degradation in terms of running times. We believe that offloading NWDAF tasks to the cloud allows telcos to focus on expanding their core capabilities, and reduce capital and operational expenditures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".