用大模型统一管理复杂权限，决策可解释且准确率超98%
LLMAC: A Global and Explainable Access Control Framework with Large Language Model
- 用大模型融合角色、属性、自主访问控制，构建统一框架
- 在合成数据上达98.5%准确率，远超传统方法
- 适合需要动态权限管理和透明决策的企业安全系统
当前企业需应对超越传统方法的复杂动态安全需求。现有角色基础（RBAC）、属性基础（ABAC）和自主访问控制（DAC）难以适应现代系统中的情境化工作流。本文提出基于大语言模型（LLM）的统一访问控制框架 LLMAC，整合多种控制机制。采用大规模合成数据集，涵盖所有权验证、版本管理、工作流及动态角色分离等场景。使用 Mistral 7B 训练模型，在测试中达到 98.5% 准确率，显著优于传统方法（RBAC: 14.5%，ABAC: 58.5%，DAC: 27.5%），并生成人类可读的决策解释。系统具备合理响应时间与资源开销，具备实际部署可行性。
原文摘要 · Abstract (English)
Today's business organizations need access control systems that can handle complex, changing security requirements that go beyond what traditional methods can manage. Current approaches, such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Discretionary Access Control (DAC), were designed for specific purposes. They cannot effectively manage the dynamic, situation-dependent workflows that modern systems require. In this research, we introduce LLMAC, a new unified approach using Large Language Models (LLMs) to combine these different access control methods into one comprehensive, understandable system. We used an extensive synthetic dataset that represents complex real-world scenarios, including policies for ownership verification, version management, workflow processes, and dynamic role separation. Using Mistral 7B, our trained LLM model achieved outstanding results with 98.5% accuracy, significantly outperforming traditional methods (RBAC: 14.5%, ABAC: 58.5%, DAC: 27.5%) while providing clear, human readable explanations for each decision. Performance testing shows that the system can be practically deployed with reasonable response times and computing resources.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。