Automatic discovery of botnet communities on large-scale communication networks
Bibliographic record
Abstract
Botnets are networks of compromised computers infected with malicious code that can be controlled remotely under a common command and control (C&C) channel. Recognized as one the most serious security threats on current Internet infrastructure, advanced botnets are hidden not only in existing well known network applications (e.g. IRC, HTTP, or Peer-to-Peer) but also in some unknown or novel (creative) applications, which makes the botnet detection a challenging problem. Most current attempts for detecting botnets are to examine traffic content for bot signatures on selected network links or by setting up honeypots. In this paper, we propose a new hierarchical framework to automatically discover botnets on a large-scale WiFi ISP network, in which we first classify the network traffic into different application communities by using payload signatures and a novel cross-association clustering algorithm, and then on each obtained application community, we analyze the temporal-frequent characteristics of flows that lead to the differentiation of malicious channels created by bots from normal traffic generated by human beings. We evaluate our approach with about 100 million flows collected over three consecutive days on a large-scale WiFi ISP network and results show the proposed approach successfully detects two types of botnet application flows (i.e. Blackenergy HTTP bot and Kaiten IRC bot) from about 100 million flows with a high detection rate and an acceptable 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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".