Automated Hyperparameter Tuning and Ensemble Machine Learning Approach for Network Traffic Classification
Bibliographic record
Abstract
Network traffic analysis and forecasting have become indispensable to dynamically allocate bandwidth, congestion control, security, and planning in today’s complex, heterogeneous, and traffic-intensive networks. This study focuses on leveraging and optimizing machine learning techniques to perform network traffic analysis and classification for decision-making. The study initiated with a thorough exploration of machine learning algorithms. Various algorithms, including Logistic Regression, SVM, KNN, Decision Trees, Random Forests, Extra Trees, Bagging, and AdaBoost, are applied for multi-class classification in network traffic analysis. To improve accuracy, we first employed automated hyperparameter tuning for each of the algorithms. After that we implemented the ensemble method with unanimous voting. In terms of unanimous voting, increasing the number of algorithms with certain criteria correlates with a decrease in false positives and false negatives. The results of our approach clearly improve the accuracy of the network traffic classification over fundamental machine learning models for better management of complex heterogeneous networks.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".