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

TL;DR

MuSimA is a web-based tool that generates large-scale synthetic ABAC datasets with customizable attribute distributions, supporting multi-modal input including JSON specifications and sketches, to aid research scalability testing.

Contribution

We introduce MuSimA, a novel tool enabling flexible, multi-modal generation of synthetic ABAC datasets for research and testing purposes.

Findings

Supports multi-modal input including JSON and sketches

Uses LLM to extract distribution parameters from sketches

Allows scalable dataset generation for ABAC research

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 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.