Defaming Botnet Toolkits: A Bottom-Up Approach to Mitigating the Threat
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 have become one of the most prevailing threats to today's Internet partly due to the underlying economic incentives of operating one. Botnet toolkits sold by their authors allow any layman to generate his/her own customized botnet and become a botmaster; botnet services sold by botmasters allow any criminal to steal identities and credit card information; finally, such stolen credentials are sold to end-users to make unauthorized transactions. Many existing botnet countermeasures meet inherent difficulties when they choose to target the botmasters or authors of toolkits, because those at the highest levels of this food chain are also the most technology-savvy and elusive. In this paper, we propose a different, bottom-up approach. That is, we defame botnet toolkits through discouraging or prosecuting the end-users of the stolen credentials. To make the concept concrete, we present a case study of applying the approach to a popular botnet toolkit, Zeus, with two methodologies, namely, reverse engineering and behavioural analysis.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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