A hybrid approach to operating system discovery based on diagnosis theory
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
Motivated by the increasing importance of knowing which operating systems are running in a given network, we evaluated operating system discovery (OSD) tools. The results indicated a serious lack of accuracy in current OSD tools. This thesis proposes a new approach to OS discovery which addresses the limitations of existing tools and leads to a more flexible, less intrusive, and much more accurate tool. Moreover, unlike existing OSD tools which are completely ad hoc, our approach is formal and follows the principles of diagnosis problem solving. This formalism allows us to: (a) characterize the complexity of OSD; (b) use well-tested algorithms and (c) benefit from numerous possible extensions. To fully address the needs of OSD, we generalize the theory of diagnosis with a query-based extension. This extension leads to a spectrum of test selection algorithms to solve each query.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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