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MuSimA: A Tool with Multi-modal Input for Generating Bespoke ABAC Datasets

arXiv Security Archived Apr 14, 2026 ✓ Full text saved

arXiv:2604.10501v1 Announce Type: new Abstract: Recent advances in research on Attribute-based Access Control (ABAC) has led to the development of several ingenious methods for representing and enforcing organizational security policies. However, so far little effort has been spent towards building a tool for generating large-scale synthetic datasets that can be used to test the developed ABAC systems. In this paper, we address this shortcoming by building MuSimA - a web-based tool for generatin

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    Computer Science > Cryptography and Security [Submitted on 12 Apr 2026] MuSimA: A Tool with Multi-modal Input for Generating Bespoke ABAC Datasets Saket Jha (Indian Institute of Technology Kharagpur, India), Karthikeya S. M. Yelisetty (Indian Institute of Technology Kharagpur, India), Singabattu Sathya (Indian Institute of Technology Kharagpur, India), Shamik Sural (Indian Institute of Technology Kharagpur, India) Recent advances in research on Attribute-based Access Control (ABAC) has led to the development of several ingenious methods for representing and enforcing organizational security policies. However, so far little effort has been spent towards building a tool for generating large-scale synthetic datasets that can be used to test the developed ABAC systems. In this paper, we address this shortcoming by building MuSimA - a web-based tool for generating ABAC datasets with user-specified probability distributions of attribute values. It supports multi-modal input, i.e., users can provide specifications either as a structured JSON file or as a combination of a minimal JSON along with hand-drawn distribution sketches. In the latter case, a Large Language Model is used to automatically extract appropriate distribution parameters from the sketches. The generated synthetic ABAC data matching the input specifications can be downloaded by the user. For studying scalability of algorithms and methods related to ABAC, data can be generated for varying sizes and complexities. We make MuSimA freely available for use by the research community. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.10501 [cs.CR]   (or arXiv:2604.10501v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.10501 Focus to learn more Submission history From: Shamik Sural [view email] [v1] Sun, 12 Apr 2026 07:27:48 UTC (1,732 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 14, 2026
    Archived
    Apr 14, 2026
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