智能动态网络安全风险管理框架,具有AI模型的可解释性和可解释性,以提高数字基础设施的安全性和弹性
Shareeful Islam1, Nihala Basheer1, Spyridon Papastergiou2,3
1School of Computing and Information Science, Anglia Ruskin University, East Road, Cambridge, UK.
概括
组织面临越来越多的网络攻击. 一个新的动态网络安全风险管理 (d-CSRM) 框架使用人工智能通过考虑动态因素来优先考虑漏洞,增强风险评估和决策.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 风险管理 风险管理
背景情况:
- 网络攻击日益复杂,对组织构成持续威胁.
- 有效的网络安全风险管理至关重要,但受到动态基础设施变化和不断变化的威胁的挑战.
- 现有的支持人工智能的风险管理往往缺乏与动态风险因素的整合.
研究的目的:
- 提出一个新的动态网络安全风险管理 (d-CSRM) 框架.
- 将诸如漏洞利用和资产依赖等动态参数纳入风险评估.
- 加强应对网络风险的决策.
主要方法:
- 开发了一个d-CSRM的系统过程.
- 利用混合人工智能模型,将线性回归和深度学习结合起来,以优先考虑漏洞.
- 将可解释性和可解释性集成到AI模型中,以实现透明的决策.
主要成果:
- 混合人工智能模型有效地优先考虑了CVEjoin数据集中的漏洞.
- 开发类型,平台和影响等关键特征被确定为动态风险评估的关键.
- 该框架成功量化了具有可解释性的动态风险.
结论:
- 拟议的d-CSRM框架解决了静态风险管理的局限性.
- 混合人工智能模型提供了有效和可解释的漏洞优先级.
- 这种方法提高了组织为动态网络安全风险管理做出明智决策的能力.
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