Bibliographic record
Abstract
Abstract : Air Force Doctrine Document 3-12 defines cyberspace as a global domain within the information environment consisting of the interdependent network of information technology infrastructures, including the Internet, telecommunications networks, computer systems, and embedded processors and controllers. Cyberspace attacks are on the rise. In September 2012, hackers attacked Canadian energy companies that manage 60 percent of all oil and gas pipelines in North and Latin America. At about the same time, sophisticated hackers attacked several large United States banking institutions in cyberspace attacks that experts concluded had been planned for weeks. And the now-famous Stuxnet cyber weapon infected the software that controls Irans nuclear weapons centrifuges, resulting in what some experts believe was the first cyberspace attack targeting infrastructure control software.This paper will analyze the application of the law of armed conflict to cyber operations, applying jus ad bellum principles to cyber operations in order to determine when a cyberspace attack constitutes a use of force. Then, applying the jus ad bellum principles to the Stuxnet worm, the paper argues that Stuxnet constituted a use of force under Article 2(4) of the United Nations Charter. Further, analyzing the jus in bello principles of proportionality, military necessity, and discrimination, the paper concludes that the Stuxnet attack complied with all applicable principles of international humanitarian law.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".