相关实验视频
OntoSecAI:用于支持人工智能的系统的本体学驱动的安全自动化
Ubaid Ullah1, Muhammad Haleem2, Asad Ullah3
1IMT School for Advanced Studies, Lucca, Italy.
PloS one
|December 18, 2025
概括
本研究介绍了OntoSecAI,这是一种基于本体学的方法,用于自动化人工智能 (AI) 威胁建模. 它通过提供一致的风险评估和各种AI系统的统一知识库来解决AI安全风险.
科学领域:
- 计算机科学 计算机科学
- 人工智能安全 的人工智能安全
背景情况:
- 人工智能 (AI) 模型引入了安全风险,包括恶意数据利用.
- 基于人工智能的威胁建模研究受到数据表示,推断规则和评估挑战的限制.
- 利益相关者的AI和安全专业知识不足导致不准确的威胁建模.
研究的目的:
- 开发和实施OntoSecAI,一种基于本体学的方法,用于自动化威胁建模和评估支持人工智能的系统.
- 解决正式数据表示,自动威胁识别和对AI安全的一致风险评估的局限性.
主要方法:
- 为人工智能安全和数据表示设计了三个本体和30个推理规则.
- 实施基于CVSS的风险和漏洞评估,用于全面的威胁建模.
- 通过10个案例研究验证了该方法,并使用数学定理进行了验证.
主要成果:
- 实体学为安全和人工智能系统提供了统一的表示和全面的数据覆盖.
- 推断规则有效地将系统资产映射到潜在的安全威胁中.
- 实体学利用确保在AI系统中一致的风险和漏洞评估.
结论:
- 对于具有不同专业知识的利益相关者来说,OntoSecAI提供了一个全面的安全知识库.
- 该方法确保在各种AI系统中统一的威胁建模.
- OntoSecAI提高了对AI中新出现的安全威胁的适应能力.
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