Performance and economies of ‘bot-less’ application-layer DDoS attacks
Bibliographic record
Abstract
An interesting new trend pertaining to application-layer DDoS is the so-called `bot-less' attack execution, in which - instead of a network of compromised computers (i.e., a network of bots/zombies) - the browsers of legitimate/non-infected computers are manipulated into generating the attack traffic. In this paper, we give an overview of two different forms of `bot-less' application-layer DDoS attacks - one conducted by means of the so-called puppetnets, and the other by means of spam email with Web-bugs (as recently evaluated in our study [1]). In particular, we take the perspective of a potential DDoS attacker, and discuss the major pros and cons of each of these alternative attack approaches from the point of view of their performance, as well as from the point of view of their cost. We clearly identify scenarios when the use of `bot-less' DDoS attack mechanisms may be preferred over the use of botnets. To the best of our knowledge, this paper is the first one to offer a comprehensive look at different application-layer DDoS execution mechanisms and bring attention to a potentially growing problem of `bot-less' DDoS attacks.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".