Darknet Traffic and Application Classification Using Heterogeneous Graph Neural Network
Bibliographic record
Abstract
The proliferation of Virtual Private Networks (VPNs) and The Onion Router (TOR) has both benefits and drawbacks for individuals and organisations.These technologies offer enhanced privacy and security online, but can also facilitate illegal or harmful behaviour by masking users' identities.Therefore, it is crucial to develop reliable methods for identifying and monitoring VPN and TOR traffic to mitigate potential risks and ensure online safety.In this paper, we propose a Darknet Heterogeneous Graph Neural Network (DHGNN) model to address the challenge of detecting traffic and applications in the Darknet.Our approach utilizes the CIC-Darknet2020 dataset, a large collection of openly available network traffic data, to train and evaluate our DHGNN classifier.The dataset is systematically explored to identify the most informative features and preprocessed into a clean tabular format.This tabular data is then converted into a graph structure suitable for the DHGNN classifier.Experimental results show that the proposed model achieves 99.80% accuracy in traffic classification and 98.80% accuracy in application classification, outperforming existing methods in Darknet classification.This approach demonstrates the effectiveness of integrating feature-driven preprocessing with graph-based neural network modeling for robust and accurate classification.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 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 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".