Proceedings of the 3rd international workshop on Visualization for computer security
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 3rd International Workshop on Visualization for Computer Security -- VizSEC'06 held November 3rd, 2006 at George Mason University in conjunction the Thirteenth ACM Conference on Computer and Communications Security (CCS). This year's workshop continues its tradition of being the premier forum for presentation of research results and experience reports on visualization for cybersecurity. VizSEC provides researchers and practitioners the unique opportunity of a focused workshop environment to share their ideas with others from around the world interested in applying visualization techniques to the application domain of Internet security.The VizSEC'06 call-for-papers attracted 44 submissions from Asia, Australia, Canada, Europe, and the United States. The program committee accepted 19 papers that cover a wide variety of topics. In addition to traditional VizSEC'06 strengths in system administration tools, traffic visualization, intrusion detection, and routing -- this year we have industry participation (3 papers and 8 members of the program committee), and papers on new topics such as wireless, encryption, and DNS. While the accepted papers are USA-centric, there are accepted papers from Australia, Canada, Israel, Japan, and Switzerland. Future VizSEC'06 workshops will seek to increase international participation. This year we also introduced two new paper categories: (1) update papers on tools presented at previous VizSEC'06 workshops and (2) position papers. It is our hope these two new paper categories will provide another opportunity for communicating interesting work in future VizSEC'06workshops.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.072 | 0.019 |
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".