Tabular-to-Image Transformations for the Classification of Anonymous Network Traffic Using Deep Residual Networks
Bibliographic record
Abstract
With the meteoric rise in anonymous network traffic data, there is a considerable need for effective automation in traffic identification tasks. Though many shallow and deep machine learning network traffic classification solutions have been proposed, they often rely on tabular data, making them unable to detect complex spatial relationships. However, recent advancements in computer processing power have increased the viability of transforming tabular data into images for training deep convolutional neural networks, transforming structured data problems into spatial ones. To identify the most effective methods for representing tabular anonymous network traffic data as images, we compared five deep learning classifiers trained on data from different tabular-to-image algorithms–Image Generator for Tabular Data (IGTD), DeepInsight, vector-of-feature wrapping (normalized and non-normalized), and our newly introduced Binary Image Encoding (BIE) technique in the classification of eight network application types. Furthermore, we examine whether deep residual models trained on tabular-to-image data can outperform the top-performing shallow learner, XGBoost, at classifying anonymous network traffic. We found that ResNet-50, a pre-trained instance of deep residual network, trained on image datasets using IGTD and the novel Binary Image Encoding outperformed XGBoost trained on tabular data. Our ResNet-50 models trained using IGTD and BIE achieved F1-scores of 96.0% and 98.49% respectively, improving on the baseline of 95.1% achieved by XGBoost.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".