Dark Web Traffic Detection Using Supervised Machine Learning
Bibliographic record
Abstract
This paper presents a study on the application of supervised machine learning algorithms for the purpose of distinguishing and categorizing Virtual Private Network (VPN) and The Onion Router (TOR) traffic on the dark web. The dark web, characterized by its anonymity and inaccessibility, has become a popular platform for illicit activities such as drug trafficking, money laundering, and cybercrime. While VPNs and TOR can be used for legitimate purposes such as privacy protection and bypassing internet censorship, they can also be exploited by cybercriminals. The CIC-Darknet2020 dataset, which includes a comprehensive collection of network traffic captures from the dark web incorporating traffic features from both VPN and TOR technologies, is used for this study. We employ classification algorithms such as Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree classifiers to construct our model. The performance of the model is evaluated using parameters such as execution time, accuracy, precision, F-measure, and recall, utilizing five-fold and ten-fold cross-validation and 66/34 and 80/20 percentage splits. Our results show that the Decision Tree (J48) classifier outperforms other classifiers, achieving 99.6% accuracy with an execution time of 15 seconds for ten-fold cross-validation. The findings of this study have implications for enhancing cybersecurity measures in identifying and mitigating threats associated with VPN and TOR traffic on the dark web.
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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 | 0.000 | 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 | 0.001 |
| 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 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".