Encrypted Network Traffic Classification using Ensemble Learning Techniques
Bibliographic record
Abstract
There is a continuous evolution of technological devices leading to a huge amount of traffic data on the internet.This presents Internet Service Providers with changes in the Quality of Service being provided and network security.The classification of these network traffic data promotes a better QoS, and management of the encrypted network.The major concern of the ISPs is protecting users' privacy, thereby generating network traffic data that are encrypted.In this thesis, we determine the best techniques as well as the relevant statistical features suitable for the classification of the non-VPN encrypted network traffic data.We utilize the opensource UNB and the Solana Networks encrypted network traffic datasets.We performed multiple experiments that led to developing an ensemble learning model with the stacking technique using the deep learning and machine learning classification algorithms with the best performances for the classification of the non-VPN encrypted network traffic data.My profound gratitude goes to God Almighty for His estimable and inexplicable love and cares all these years.The completion of this work could not have been possible without His infinite mercies, strength, and wisdom.Thus, I will ever remain grateful to Him. My special thanks and appreciation
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".