Empirical Evidence for Non-equilibrium Behaviors within Peer-to-Peer Structured Botnets
Bibliographic record
Abstract
Although we have become adept at taking-down individual botnets, the global botnet threat has remained largely unabated, particularly if one considers the more recent generation of peer-to-peer (P2P) structured botnets. A potential formal explanation for this dichotomy is that P2P botnets simply fail to behave as statistically equilibrium systems, (i.e., as systems possessing singular statistical steady-states). Equilibrium assumptions have been commonly applied in the construction of botnet defenses, but these assumption have gone untested. This work shows empirically via standard Monte Carlo packet-level simulations that well studied Kademlia P2P botnet protocol can easily produce both statistically non-stationary and non-ergodic behaviors once the Internet routing processes are modeled. Moreover, it is shown that by re-tuning a botnet's run-time parameters a botmaster can make the botnet behave as a non-stationary process from the defender's perspective. More formally, this work provides empirical evidence that network level botnet detection features need not be measure invariant as has generally been presupposed.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".