An Investigation on Detecting Applications Hidden in SSL Streams using Machine Learning Techniques
Bibliographic record
Abstract
The importance of knowing what type of traffic is flowing through a network is\nparamount to its success. Traffic shaping, Quality of Service, identifying critical\nbusiness applications, Intrusion Detection Systems, as well as network administra-\ntion activities all require the base knowledge of what traffic is flowing over a network\nbefore any further steps can be taken. With SSL traffic on the rise due to applica-\ntions securing or concealing their traffic, the ability to determine what applications\nare running within a network is getting more and more difficult. Traditional methods\nof traffic classification through port numbers or deep packet inspection have been\ndeemed inadequate by researchers thus making way for new methods. The purpose\nof this thesis is to investigate if a machine learning approach can be used with flow\nfeatures to identify SSL in a given network trace. To this end, different machine\nlearning methods are investigated without the use of port numbers, Internet Protocol\naddresses, or payload information. Various machine learning models are investigated\nincluding AdaBoost, Naive Bayes, RIPPER, and C4.5. The robustness of the results\nare tested against unseen datasets during training. Moreover, the proposed approach\nis compared to the Wireshark traffic analysis tool. Results show that the proposed ap-\nproach is very promising in identifying SSL traffic from a given network trace without\nusing port numbers, Internet protocol addresses, or payload information.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".