A flow based approach for SSH traffic detection
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
The basic objective of this work is to assess the utility of two supervised learning algorithms AdaBoost and RIPPER for classifying SSH traffic from log files without using features such as payload, IP addresses and source/destination ports. Pre-processing is applied to the traffic data to express as traffic flows. Results of 10-fold cross validation for each learning algorithm indicate that a detection rate of 99% and a false positive rate of 0.7% can be achieved using RIPPER. Moreover, promising preliminary results were obtained when RIPPER was employed to identify which service was running over SSH. Thus, it is possible to detect SSH traffic with high accuracy without using features such as payload, IP addresses and source/destination ports, where this represents a particularly useful characteristic when requiring generic, scalable solutions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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