A Complete EDA and DL Pipeline for Softwarized 5G Network Intrusion Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rise of 5G networks is driven by increasing deployments of IoT devices and expanding mobile and fixed broadband subscriptions. Concurrently, the deployment of 5G networks has led to a surge in network-related attacks, due to expanded attack surfaces. Machine learning (ML), particularly deep learning (DL), has emerged as a promising tool for addressing these security challenges in 5G networks. To that end, this work proposed an exploratory data analysis (EDA) and DL-based framework designed for 5G network intrusion detection. The approach aimed to better understand dataset characteristics, implement a DL-based detection pipeline, and evaluate its performance against existing methodologies. Experimental results using the 5G-NIDD dataset showed that the proposed DL-based models had extremely high intrusion detection and attack identification capabilities (above 99.5% and outperforming other models from the literature), while having a reasonable prediction time. This highlights their effectiveness and efficiency for such tasks in softwarized 5G environments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it