Botnet Detection System Analysis on the Effect of Botnet Evolution and Feature Representation
Bibliographic record
Abstract
Botnets are known as one of the main destructive threats that have been active since 2003 in various forms. The ability to upgrade the structure and algorithms on the fly is part of what causes botnets to survive for more than a decade. Hence, one of the main concerns in designing a botnet detection system is how long such a system can be effective and useful considering the evolution of a given botnet. Furthermore, the data representation and the feature extraction components have always been an important issue in order to design a robust detection system. In this work, we employ machine learning algorithms (genetic programming and decision trees) to explore two questions: (i) How can the representation of non-numeric features effect the detection system's performance? and (ii) How long can a machine learning based detection system can perform effectively? To this end, we gathered seven Zeus botnet data sets over a period of four years and analyzed three different data representation techniques to be able to explore aforementioned questions.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".