Owned, Pwned or Rented: Whose Domain Is It?
Bibliographic record
Abstract
Phishing attacks continue to be a persistent threat, leading researchers to develop various detection approaches that leverage data sources like Certificate Transparency (CT) logs and Domain Name System (DNS) records. These techniques aim to identify newly registered domains associated with phishing campaigns. However, the extent to which these methods can effectively cover the diverse landscape of phishing activities remains unclear. This paper seeks to quantify the proportion of phishing attacks that utilize dedicated, attacker-owned domains - the primary target of techniques based on CT logs and domain registration data. By developing a taxonomy of phishing website ownership types, this study analyzes the percentage of phishing attacks that involve domains directly controlled by the attackers, versus those hosted on compromised or third-party platforms. This analysis provides valuable insights into the diverse phishing domain ownership patterns. The findings can guide the security community in developing more comprehensive solutions to mitigate this persistent threat.
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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 | 0.001 |
| 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) | 0.001 | 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 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".