Network Traffic Classification Using Self-Supervised Learning and Confident Learning
Bibliographic record
Abstract
Network traffic classification (NTC) is vital for efficient network management, security, and performance optimization, particularly with 5G/6G technologies. Traditional methods, such as deep packet inspection (DPI) and port-based identification, struggle with the rise of encrypted traffic and dynamic port allocations. Supervised learning methods provide viable alternatives but rely on large labeled datasets, which are difficult to acquire given the diversity and volume of network traffic. Meanwhile, unsupervised learning methods, while less reliant on labeled data, often exhibit lower accuracy. To address these limitations, we propose a novel framework that first leverages Self-Supervised Learning (SSL) with techniques such as autoencoders (AE) or Tabular Contrastive Learning (TabCL) to generate pseudolabels from extensive unlabeled datasets, addressing the challenge of limited labeled data. We then apply traffic-adapted Confident Learning (CL) to refine these pseudo-labels, enhancing classification precision by mitigating the impact of noise. Our proposed framework offers a generalizable solution that minimizes the need for extensive labeled data while delivering high accuracy. Extensive simulations and evaluations using three datasets (ISCX VPN-nonVPN, self-generated dataset, and UCDavis–X;QUIC) demonstrate that our method achieves superior accuracy compared to state-of-the-art techniques in classifying network traffic.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".