智能水系统的最佳网络安全框架:检测,定位和严重性评估
1Civil & Environmental Engineering Department, Lehigh University, 27 Memorial Drive West, Bethlehem, PA 18015, USA.
Water research
|April 1, 2025
概括
本研究介绍了一个网络安全框架,用于检测和定位智能供水系统中的网络攻击. 这种新的方法提高了安全性,并通过在最短的时间内准确识别威胁来帮助资源规划.
科学领域:
- 网络安全 网络安全
- 水系统工程 水系统工程
- 机器学习 机器学习
背景情况:
- 水分系统的数字化转型增加了网络漏洞.
- 智能设备 (传感器,计量器) 的连接性会产生安全风险.
- 需要采取先进的安全措施,防止对关键基础设施的网络攻击.
研究的目的:
- 为水分系统提出一个全面的网络安全框架.
- 开发用于网络攻击检测,定位和影响评估的方法.
- 提高智能水网的安全性和弹性.
主要方法:
- 实现了基于重建的最佳网络攻击探测器:自动编码器和1D CNN.
- 使用贝叶斯优化优化检测器.
- 使用Savitzky-Golay过器进行后处理,以减少虚假报警.
- 开发了一个攻击本地化框架和严重程度指数.
主要成果:
- 在BATADAL基准中成功检测到所有网络攻击,STTD>>>>98% .
- 在自动编码器和CNN模型中实现了>95%的联合检测准确度.
- 在检测准确度和最小化检测延迟方面表现优于现有模型.
- 在C-Town基准上展示了有效的攻击本地化和严重性评估.
结论:
- 拟议的框架为保护智能供水系统提供了一个强大的解决方案.
- 这种方法显著提高了网络攻击检测和本地化能力.
- 开发的严重程度指数有助于有效的资源规划和为水网安全做出决策.
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