BERT-Driven Intrusion Detection System for 5G Core Security
Bibliographic record
Abstract
The proliferation of $\mathbf{5 G}$ networks introduces a highly dynamic and virtualized service-based architecture, increasing the attack surface of core network components. Securing the 5 G Core requires accurate and scalable intrusion detection mechanisms. This work proposes a BERT-based Intrusion Detection System (IDS) designed to classify legitimate and malicious 5G Core communications. A cloud-native testbed was deployed using Free5GC, UERANSIM, and Kubernetes to emulate realistic 5 G core operations. Legitimate traffic was captured from user equipment (UE) authentication procedures, while malicious flows were generated from three control-plane attack scenarios: TCP SYN flooding, PFCP session deletion, and unauthorized NRF probing. Compared to baseline mod-els-LSTM and 1D CNN-LSTM-BERT achieved superior detection performance, attaining 100% accuracy, precision, recall, and F1-score. However, computational analysis indicated that BERT’s resource demands, including over 3 GB of GPU memory and 266.52 GFLOPs per inference, may limit deployment in constrained environments. Future work will investigate more computationally efficient BERT variants for practical deployment in cloud-native 5 G security frameworks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".