Automated implementation of windows-related security-configuration guides
Patrick Stöckle, Bernd Grobauer, Alexander Pretschner
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
Hardening is the process of configuring IT systems to ensure the security of\nthe systems' components and data they process or store. The complexity of\ncontemporary IT infrastructures, however, renders manual security hardening and\nmaintenance a daunting task.\n In many organizations, security-configuration guides expressed in the SCAP\n(Security Content Automation Protocol) are used as a basis for hardening, but\n ...
these guides by themselves provide no means for automatically implementing the\nrequired configurations.\n In this paper, we propose an approach to automatically extract the relevant\ninformation from publicly available security-configuration guides for Windows\noperating systems using natural language processing. In a second step, the\nextracted information is verified using the information of available settings\nstored in the Windows Administrative Template files, in which the majority of\nWindows configuration settings is defined.\n We show that our implementation of this approach can extract and implement\n83% of the rules without any manual effort and 96% with minimal manual effort.\nFurthermore, we conduct a study with 12 state-of-the-art guides consisting of\n2014 rules with automatic checks and show that our tooling can implement at\nleast 97% of them correctly. We have thus significantly reduced the effort of\nsecuring systems based on existing security-configuration guides.\n
Results & Benchmarks
No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.
Hardening is the process of configuring IT systems to ensure the security of\nthe systems' components and data they process or store.
Implementation Evidence Summary
Recommendation evidence is currently too limited for a maintained-repo choice. Use Implementation Status and Reproduction Path for a practical baseline plan.
Reproduction Risks
- Estimate is based on paper-only reproduction flow
Hardware Notes
Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Evidence disclosure
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation Status
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: Representing Stages and Levels of Automation on a Decision Ladder.
- Track assumptions and missing details in an experiment log before coding.
Reproduction readiness
Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
No verified implementation available
- · No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.
No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet.
Continue with targeted Hugging Face searches derived from the paper title and method context:
Datasets
Spaces
Tip: start with models, then check datasets/spaces if you need evaluation data or demos.
Direct artifact matches are currently sparse. Use targeted Hugging Face searches to quickly locate candidate models, datasets, and demos.
Research context
8
Citations
34
References
Tasks
Computer science, Automation, Process (computing), Task (project management), Software engineering, Database, Operating system
Methods
None detected
Domains
Computer security
Evaluation & Human Feedback Data
Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.
Open in HFEPXExplore Similar Papers
Jump to Paper2Code search queries derived from this paper's research context.
Related papers
-
Search on Paper2Code
Representing Stages and Levels of Automation on a Decision Ladder (2016) Semantic similarity
-
Search on Paper2Code
Universal Command Guide: For Operating Systems (2002) Semantic similarity
-
Search on Paper2Code
Automation of Sensor Control in Uninhabited Aerial Vehicles (2015) Semantic similarity
-
Search on Paper2Code
What avionics engineers should know about pilots and automation (2002) Semantic similarity
-
Search on Paper2Code
Impacts of Automation on Precision (2009) Semantic similarity
-
Search on Paper2Code
Human-Centered Challenges and Contribution for the Implementation of Automated Driving (2011) Semantic similarity
Need human evaluators for your AI research? Scale annotation with expert AI Trainers.