Exploring Visible Internet Hosts through Census and Survey
Bibliographic record
Abstract
Measurement studies published in the literature have, for the most part, ignored the population of hosts. While many hosts are hidden behind firewalls and in private address space, there is much to be learned from examining the population of visible Internet hosts—one can better understand network growth and accessibility and this understanding can help to assess vulnerabilities, deployment of new technologies, and improve network models. This paper is, to our knowledge, the first attempt to measure the population of visible Internet edge hosts. We measure hosts in two ways: via periodic Internet censuses, where we query all accessible Internet addresses every few months, and via surveys of a small fraction of the responsive address space, probing each address every 11 minutes for one week. These approaches are complementary: a census is effective at evaluating the Internet as a whole, while surveys validate the census and allow observation of the lifetime of typical address occupancy. We find that only 3.6 % of allocated addresses are actually occupied by visible hosts, and that occupancy is unevenly distributed, with a quarter of responsive /24 subnets less than 5 % full, and only 9 % of subnets more than half full. We establish an upper-bound on the number of servers in the Internet at 36 million, about 16 % of the responsive addresses. Many firewalls are visible and we observe significant diversity in the distribution of firewalled block size. While the absolute number of firewalled blocks appears stable, the ratio of coverage of visible firewalls to the number of visible addresses is declining, perhaps suggesting increasing use of invisible firewalls. 1
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.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".