LIDarknet: Experimenting the Power of Ensemble Learning in the Classification of Network Traffic
Bibliographic record
Abstract
The Darknet is an encrypted corner of the internet, intended for users who wish to remain anonymous and mask their identity. Because of its anonymous qualities, the Darknet has become a go-to platform for illicit activities such as drug trafficking, terrorism, and dark marketplaces. Therefore, it is important to recognize Darknet traffic in order to monitor and detect malicious online activities. This paper investigates the potential effectiveness of machine learning algorithms in identifying attacks using the CICdarknet2020 dataset. The dataset includes two distinct classification targets: traffic label and application labels. The objective of our research is to identify optimal classifiers for traffic and application classification by employing ensemble learning methods, aiming to achieve the highest possible results. Through our experimentation, we have found that the best-performing models surpassing all other state-of-the-art machine learning models are LightGBM, achieving a 93.41% f1-score in the Application classification, and Random Forest, achieving a 99.8% f1-score in the traffic classification.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".