Assessing Trade-Offs between Stealthiness and Node Recruitment Rates in Peer-to-Peer Botnets
Bibliographic record
Abstract
Botnets denote collections of compromised computers under adversary control and, although early botnets using centralized command and control (C&C) structures were fairly easily defeated, botnets remain a serious global security threat. in part, this is due to the evolution within the adversarial communities using highly diffuse decentralized peer-to-peer (P2P) based C&C within modern botnets, which has proven far more difficult to address. the resulting increased botnet resilience though comes at the cost of placing the bots further from the botmasterâs direct control, thereby, increasing the time required to recruit subsets of bots to specific malicious tasks, (i.e., to send spam, engage in a DDOS attack, etc.). This work explores the specific tradeoffs that occur between achievable bot recruitment rates and overall botnet stealthiness within P2P structured botnets. It is shown that rapid recruitment of nodes (or bots) leads directly to an order of magnitude increase in the botnetâs generated network traffic, which makes the botnet significantly more visible (and susceptible) to defensive counter-measures. Kademlia is used through out this work as the exemplar P2P protocol as, within the real-world, Kademlia has proven to provide an effective C&C mechanism for a number of the longer-lived botnets.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".