6Scan: A High-Efficiency Dynamic Internet-Wide IPv6 Scanner With Regional Encoding
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
Efficient Internet-wide scanning plays a vital role in network measurement and cybersecurity analysis. While Internet-wide IPv4 scanning is a solved problem, Internet-wide scanning for IPv6 is still a mission yet to be accomplished due to its vast address space. To tackle this challenge, IPv6 scanning generally needs to use pre-defined seed addresses to guide further IPv6 scanning directions. Under this general principle, various solutions have been developed, but all suffer from two primary pitfalls, low hit rate and low probing speed, caused by the inherent sparse distribution of active IPv6 addresses and the high computational complexity of the search algorithms, respectively. We develop 6Scan, a novel asynchronous IPv6 scanner that effectively addresses the above two problems. To increase the hit rate, 6Scan infers the promising search directions by encoding the regional identifiers of the target addresses within the probing packets and recording the regional activities from the asynchronously arrived replies. It then dynamically adjusts the search directions according to the scanning result of the previous steps. To speed up the search algorithm, 6Scan leverages the regional identifier encoding to quickly adjust search direction without excessive computation. Real-world experiments over the IPv6 Internet in a billion-scale probing budget show that compared with the state-of-the-art solutions, on average 6Scan can discover 6% more active addresses with nearly the same scanning time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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