Benchmarking the Effect of Flow Exporters and Protocol Filters on Botnet Traffic Classification
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
Botnets represent one of the most aggressive threats against cyber security. Different techniques using different feature sets have been proposed for botnet traffic analysis and classification. However, no work has been performed to study the effect of such differences. In this paper, we perform a study on the effect of (if any) the feature sets of network traffic flow exporters. To this end, we explore five different traffic flow exporters (each with a different set of flow features) using two different protocol filters [Hypertext Transfer Protocol (HTTP) and Domain Name System (DNS)] and five different classifiers. We evaluate all these on eight different botnet traffic data sets. Our results indicate that the use of a flow exporter and a protocol filter indeed has an effect on the performance of botnet traffic classification. Experimental results show that the best performance is achieved using Tranalyzer flow exporter and HTTP filter with the C4.5 classifier.
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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 | 0.002 | 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