Intelligent Security: Is "AI for Cybersecurity" a Blessing or a Curse (Dagstuhl Seminar 22412)
Bibliographic record
Abstract
This report documents the outcomes of Dagstuhl Seminar 22412 "Intelligent Security: Is "AI for Cybersecurity" a Blessing or a Curse". The seminar brought together 25 attendees from 10 countries (Canada, Croatia, Czech Republic, France, Germany, Netherlands, Singapore, Sweden, Switzerland, and the USA). There were 17 male and 8 female participants. Three participants were from the industry, and the rest were from academia. The gathered researchers are actively working in the domains of artificial intelligence and cybersecurity, emphasizing hardware security, fuzzing, physical security, and network security. The seminar aims to foster sharing experiences and best practices between various cybersecurity applications and understand how and when certain approaches are transferable. The first two days were devoted to 20-minute self-introductions by participants to achieve these goals. At the end of the second day, we made a list of topics that were decided to be the focus of the seminar and that will be discussed in the groups in the next few days. On the third and fourth days, the work was conducted in four discussion groups where at the end of each day, all participants gathered to report the results from the discussion groups and to align the goals. On the last day, we again worked in one group to summarize the findings and foster networking among participants. A hike was organized in the afternoon of the third day. The seminar was a success. The participants actively participated in the working groups and the discussions and went home with new ideas and collaborators. This report gathers the abstracts of the presented talks and the conclusions from the discussion groups, which we consider relevant contributions toward better interdisciplinary research on artificial intelligence and cybersecurity.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.108 | 0.073 |
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".