Smart crawlers for flash-crowd DDoS: The attacker's perspective
Bibliographic record
Abstract
Flash-crowd DDoS attacks — in which the attacking bots aim to appear indistinguishable from the regular visitors to the victim web-site — have only recently been identified in the literature. While generally seen as the most advanced and most potent type of DDoS, flash crowd attacks are only partially understood, and their practical viability is still very much unclear. To the best of our knowledge, this is the first study that takes the perspective of a potential attacker interested in executing a flash crowd DDoS, and looks at the challenges of designing a botnet that would carry out that execution effectively. The results of our study demonstrate that, through the use of some popular readily available Internet tools, the attacker is likely to succeed in harvesting critical information about any perspective victim site, and thus be in the position to customize his bots (i.e., make them behave very close to how a typical human visitor to the given site would behave). Clearly, better bot customization would imply more powerful and harder-to-defend-against DDoS attacks.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".