Search in the Expanse: Towards Active and Global IPv6 Hitlists
Bibliographic record
Abstract
Global-scale IPv6 scan, critical for network measurement and management, is still a mission to be accomplished due to its vast address space. To tackle this challenge, IPv6 scan generally leverages pre-defined seed addresses to guide search directions. Under this general principle, however, the core problem of effectively using the seeds is largely open. In this work, we propose a novel IPv6 active search strategy, namely HMap6, which significantly improves the use of seeds, w.r.t. the marginal benefit, for large-scale active address discovery in various prefixes. Using a heuristic search strategy for efficient seed collection and alias prefix detection under a wide range of BGP prefixes, HMap6 can greatly expand the scan coverage. Real-world experiments over the Internet in billion-scale scans show that HMap6 can discover 29.39M unique /80 prefixes with active addresses, an 11.88% improvement over the state-of-the-art methods. Furthermore, the IPv6 hitlists from HMap6 include all-responsive IPv6 addresses with rich information. This result sharply differs from existing public IPv6 hitlists, which contain non-responsive and filtered addresses, and pushes the IPv6 hitlists from quantity to quality. To encourage and benefit further IPv6 measurement studies, we released our tool along with our IPv6 hitlists and the detected alias prefixes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.004 |
| 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".