Follow the traffic: Stopping click fraud by disrupting the value chain
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
Advertising fraud, particularly click fraud, is a growing concern for the online advertising industry. The use of click bots, malware that automatically clicks on ads to generate fraudulent traffic, has steadily increased over the last years. While the security industry has focused on detecting and removing malicious binaries associated with click bots, a better understanding of how fraudsters operate within the ad ecosystem is needed to be able to disrupt it efficiently. This paper provides a detailed dissection of the advertising fraud scheme employed by Boaxxe, a malware specializing in click fraud. By monitoring its activities during a 7-month longitudinal study, we were able to create of map of the actors involved in the ecosystem enabling this fraudulent activity. We then applied a Social Network Analysis (SNA) technique to identify the key actors of this ecosystem that could be effectively influenced in order to maximize disruption of click-fraud monetization. The results show that it would be possible to efficiently disrupt the ability of click-fraud traffic to enter the legitimate market by pressuring a limited number of these actors. We assert that this approach would produce better long term effects than the use of take downs as it renders the ecosystem unusable for monetization.
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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 | 0.000 |
| Open science | 0.001 | 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 it