Follow the traffic: Stopping click fraud by disrupting the value chain
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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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".