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Record W2913874685

Proceedings of the 3rd ACM workshop on Assurable and usable security configuration

2010· article· en· W2913874685 on OpenAlexaboutno aff
Tony Sager, Gail‐Joon Ahn, Krishna Kant, Heather Richter Lipford

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Security and Intrusion Detection
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceComputer securityIPsecNetwork securityServerVariety (cybernetics)Network security policyWorld Wide WebAccess controlSecurity through obscuritySecurity serviceCloud computing securityThe InternetSecurity information and event managementInformation securityOperating system
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0070.008
Open science0.0030.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0620.023

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.

Opus teacher head0.011
GPT teacher head0.226
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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Same topicNetwork Security and Intrusion DetectionFrench-language works237,207