Evaluation of Synthetic Data Generation Techniques in the Domain of Anonymous Traffic Classification
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
Anonymous network traffic is more pervasive than ever due to the accessibility of services such as virtual private networks (VPN) and The Onion Router (Tor). To address the need to identify and classify this traffic, machine and deep learning solutions have become the standard. However, high-performing classifiers often scale poorly when applied to real-world traffic classification due to the heavily skewed nature of network traffic data. Prior research has found synthetic data generation to be effective at alleviating concerns surrounding class imbalance, though a limited number of these techniques have been applied to the domain of anonymous network traffic detection. This work compares the ability of a Conditional Tabular Generative Adversarial Network (CTGAN), Copula Generative Adversarial Network (CopulaGAN), Variational Autoencoder (VAE), and Synthetic Minority Over-sampling Technique (SMOTE) to create viable synthetic anonymous network traffic samples. Moreover, we evaluate the performance of several shallow boosting and bagging classifiers as well as deep learning models on the synthetic data. Ultimately, we amalgamate the data generated by the GANs, VAE, and SMOTE into a comprehensive dataset dubbed CMU-SynTraffic-2022 for future research on this topic. Our findings show that SMOTE consistently outperformed the other upsampling techniques, improving classifiers’ F1-scores over the control by ~7.5% for application type characterization. Among the tested classifiers, Light Gradient Boosting Machine achieved the highest F1-score of 90.3% on eight application types.
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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 | 0.006 | 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 | 0.002 | 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