On botnet behaviour analysis using GP and C4.5
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 a destructive cyber security threat that aim to hide their malicious activities within legitimate Internet traffic. Part of what makes botnets so affective is that they often upgrade themselves over time, hence reacting to improved detection mechanisms. In addition, Internet common communication protocols (i.e. HTTP) are used for the purposes of constructing subversive communication channels. This work employs machine learning algorithms (genetic programming and decision trees) to detect distinct behaviours in various botnets. That is to say, botnets mimic legitimate HTTP traffic while actually serving botnet purposes. To this end, two different feature sets are employed and analyzed to see how differences between three botnets - Zeus, Conficker and Torpig - can be distinguished. Specific recommendations are then made regarding the utility of different feature sets and machine learning algorithms for detecting each type of botnet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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