BotCop: An Online Botnet Traffic Classifier
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
A botnet is a network of compromised computers infected with malicious code that can be controlled remotely under a common command and control (C&C) channel. As one the most serious security threats to the Internet, a botnet cannot only be implemented with existing network applications (e.g. IRC, HTTP, or Peer-to-Peer) but also can be constructed by unknown or creative applications, thus making the botnet detection a challenging problem. In this paper, we propose a new online botnet traffic classification system, called BotCop, in which the network traffic are fully classified into different application communities by using payload signatures and a novel decision tree model, and then on each obtained application community, the temporal-frequent characteristic of flows is studied and analyzed to differentiate the malicious communication traffic created by bots from normal traffic generated by human beings. We evaluate our approach with about 30 million flows collected over one day on a large-scale WiFi ISP network and results show that the proposed approach successfully detects an IRC botnet from about 30 million flows with a high detection rate and a low false alarm rate.
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 | 0.001 |
| 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