Proceedings of the 3rd ACM workshop on Assurable and usable security configuration
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 3rd ACM Workshop on Assurable & Usable Security Configuration 2010 (SafeConfig '10) held in conjunction with the 17th ACM Conference on Computer and Communications Security in Chicago, IL. The workshop is focused on the unique challenges of securing a variety of configuration data maintained by enterprise networks. A typical enterprise network might have hundreds of security appliances such as firewalls, IPSec gateways, IDS/IPS, authentication servers, authorization/ RBAC servers and crypto systems. An enterprise network may also have other non-security devices such as routers, name servers, protocol gateways, etc. These must be logically integrated into a security architecture satisfying security goals at and across multiple networks. Logical integration is accomplished by consistently setting thousands of configuration variables and rules on the devices. The configuration must be constantly adapted to optimize protection and block prospective attacks. Also, the configuration must be tuned to balance security with usability. SafeConfig'10 will give researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of security configuration. The call for papers attracted 25 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 8 regular papers and 4 short papers that cover a variety of topics, including policy management, network and infrastructure configuration, and access control and system configuration. We hope that the proceedings will serve as a valuable reference for security researchers and developers.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.023 |
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".