Optimising sybil attacks against P2P-based botnets
Bibliographic record
Abstract
Addressing and mitigating modern global-scale botnets is a pressing Internet security issue, particularly, given that these botnets are known to be provide attackers with the large-scale low-cost computing infrastructure required to engage in major spam campaigns, larger-scale phishing attacks, etc. Over time, botnets have evolved toward using decentralized peer-to-peer (P2P) command and control (C&C) infrastructures in order to increase their resilience against defender countermeasures, i.e. as seen in Storm's use of Overnet and more recently in the appearance of HTTP-tunneled P2P botnets, such as Waledac and Conficker. The obvious question is, what are effective countermeasures against these modern botnets? This work focuses on evaluating, via simulation, sybil attack-based countermeasures and how such sybil-based strategies should be tailored to allow them to both be effective and implementable on global-scales. Slower-rate sybil infection strategies with random placement of sybils are shown to be nearly as effective as higher-rate infection strategies with targeted placement. This somewhat counter-intuitive result is important, as the former strategy is easier to implement by a loosely co-ordinated collective of globally scattered defenders.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".