Smart crawlers for flash-crowd DDoS: The attacker's perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it