Optimized DenseNet Architecture for Efficient Classification of Encrypted Internet Traffic
Bibliographic record
Abstract
The increasing reliance on Internet-based services has rendered secure and efficient network traffic classification critical. Conventional methods for categorising traffic, such as port and payload methods, often struggle with the challenges posed by encrypted traffic. Deep learning techniques have emerged as a predominant method for traffic classification given their success in domains such as image recognition, document analysis, and genomics. This research proposes an enhanced DenseNet architecture that leverages deep learning to accurately classify encrypted Internet traffic categories. This approach introduces a compression layer into the DenseNet architecture to address the co-adaptation problem as a result of the information flow and optimise the accuracy of the CNN. An Intrusion detection dataset from the Canadian Institute of Cybersecurity was used to evaluate the architecture. The optimised DenseNet architecture was evaluated using metrics such as precision, recall, accuracy, F1-Score, False Positive Rate and Area under the ROC Curve. Experimental results show that the approach can distinguish various encrypted Internet traffic categories.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".