解码5G安全:走向混合威胁本体学
1Lithuanian Cybercrime Center of Excellence for Training, Research and Education (L3CE), Vilnius, 08393, Lithuania.
Open research Europe
|October 6, 2025
概括
一个新的5G混合威胁本体学提供了一个结构化的框架,用于抵御多方面的网络攻击. 这种方法优先考虑了第五代 (5G) 网络的风险降低和可持续安全.
科学领域:
- 网络安全和网络基础设施
- 本体学工程和语义网络技术
- 风险管理和弹性研究 风险管理和弹性研究
背景情况:
- 第五代 (5G) 网络是关键基础设施,为跨越网络,物理和社会政治领域的混合威胁引入了新的脆弱性.
- 现有的安全模型缺乏统一的框架,用于系统地应对针对5G基础设施的复杂混合威胁场景.
- 对5G漏洞的敌对利用对国家安全和关键服务构成重大风险.
研究的目的:
- 引入5G混合威胁本体学,这是一个用于建模和减轻5G基础设施中的混合威胁的新框架.
- 通过提出一种以实体学为导向的方法来推进5G安全,专注于通过降低风险来提高弹性.
- 为威胁情报共享和验证提供结构化,机器可读的系统.
主要方法:
- 使用语义 Web 技术开发一个结构化的框架,特别是使用 Turtle 表示的资源描述框架 (RDF).
- 整合已建立的网络安全标准和框架,包括ENISA的5G威胁分类法和STIX.
- 使用描述逻辑 (DL) 和通过Shapes Constraint Language (SHACL) 验证的对抗策略,5G漏洞和级联风险之间的关系的正式化.
主要成果:
- 5G混合威胁本体学提供了一种统一的,结构化的方法来建模针对5G基础设施的混合威胁.
- 本体论确保了机器可读性,并促进了结构化的威胁情报共享和验证.
- 它使语义推理能够理解复杂的威胁场景,并支持以弹性驱动的安全姿态.
结论:
- 5G混合威胁本体学提供了一个新的,可适应的框架,用于提高5G基础设施对不断发展的混合威胁的弹性.
- 这种方法将政策指令与运营网络安全需求联系起来,促进可持续的安全.
- 未来的发展将重点放在适应性威胁建模和动态风险评估上,以实现持续改进.
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