针对工业4.0物联网网络的基于URL的网络安全攻击的透测试和预防的事件意识智能优先级框架
Zhanserik Nurlan1,2, Dauren Gabdullayev3, Bigul Mukhametzhanova4
1Department of Computer Science, SDU University, Kaskelen, 040900, Kazakhstan. zhanserik.nur@gmail.com.
Scientific reports
|October 30, 2025
概括
本研究引入了一个事件意识框架,以优先考虑和防止工业4.0工业物联网 (IIoT) 网络中的基于URL的网络攻击. 机器学习模型有效地分类恶意URL,增强网络安全.
科学领域:
- 网络安全 网络安全
- 事物的工业互联网 (IIoT)
- 机器学习 机器学习
背景情况:
- 工业4.0在很大程度上依赖于支持物联网的智能基础设施和数字自动化.
- 统一资源定位器 (URL) 的广泛使用增加了对网络威胁的脆弱性,特别是工业物联网 (IIoT) 网络.
- 积极主动和智能安全措施对于在工业4.0中减轻基于URL的网络攻击至关重要.
研究的目的:
- 在工业4.0 IIoT环境中,为透测试和预防基于URL的网络攻击提供一个事件意识的智能优先级框架.
- 识别和优先考虑 IIoT 网络主机中的威胁.
- 利用机器学习进行准确的威胁分类和实时预防.
主要方法:
- 透测试和漏洞分析,以识别威胁.
- 事件意识,以优先考虑威胁.
- 机器学习模型 (随机森林,SVM,XGBoost) 用于URL分类.
- 实时预防的动态值机制.
主要成果:
- 随机森林在分类恶意URL方面取得了最高的准确性.
- 支持矢量机 (SVM) 平衡精度和有效召回.
- XGBoost在识别真实威胁方面表现出高的召回率.
- 该框架使基于URL的网络威胁能够更智能,更有针对性地减轻.
结论:
- 拟议的框架提高了工业4.0基础设施对基于URL的网络威胁的弹性.
- 机器学习模型显著提高了威胁检测和预防的准确性和效率.
- 事件意识和动态值是实时缓解和最小化损害的关键.
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