A Comparison of three machine learning techniques for encrypted network traffic analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work evaluates three methods for encrypted traffic analysis without using the IP addresses, port number, and payload information. To this end, binary identification of SSH vs non-SSH traffic is used as a case study since the plain text initiation of the SSH protocol allows us to obtain data sets with a reliable ground truth. The methods are subject to several tests using different export options, feature sets, and training and test traffic traces for a total of 128 different configurations. Of particular interest are test cases which that use a test set from a different network than that which the model was trained on, i.e. robustness of the trained models. Results show that the multi-objective genetic algorithm (MOGA) based trained model is able to achieve the best performance among the three methods when each approach is tested on traffic traces that are captured on the same network as the training network trace. On the other hand, C4.5 achieved the best results among the three methods when tested on traffic traces which are captured on totally different networks than the training trace. Furthermore, it is shown that continuous sampling of the training data is no better than random sampling, but the training data is very important for how well the classifiers will perform on traffic traces captured from different networks. Moreover, the C4.5 based approach provides the fastest and the most human readable model, whereas the MOGA reduces the complexity of the k-means clustering algorithm tremendously.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it