相关实验视频
Updated: Jul 18, 2025

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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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机器学习和用户界面用于水基础设施的网络风险管理
Nataliia Neshenko1, Elias Bou-Harb2, Borko Furht1
1Department of Information Technology and Operations Management, College of Business, Florida Atlantic University, Boca Raton, Florida, USA.
概括
对水电厂的网络攻击威胁到公众健康. 本研究介绍了一种结合机器学习和可视化的新方法,用于更好地管理关键水基础设施的网络风险.
科学领域:
- 环境工程 环境工程
- 网络安全 网络安全
- 风险管理 风险管理
背景情况:
- 水处理厂的现代化增加了对网络攻击的脆弱性,对公共卫生和经济稳定构成风险.
- 不断发展的网络威胁需要先进的技术来有效地管理关键水基础设施的网络风险.
- 目前的方法可能缺乏精确攻击推断,定位和影响评估所需的细节性.
研究的目的:
- 建议改进在关键水利基础设施的网络风险管理方法.
- 为了提高攻击推断,定位和影响估计的细节性.
- 通过丰富的上下文信息,促进有效的运营风险管理.
主要方法:
- 一种结合机器学习,信念函数,操作性能指标和动态可视化的新方法.
- 视觉领域意识到异常的探索,以改善检测和定位.
- 拟议方法的独立可用性:灵活应用的方法元素.
主要成果:
- 在异常检测和定位方面表现出性能改善.
- 提高了识别网络异常位置的精度.
- 通过详细的影响估计,有效地确定风险优先级.
- 弥合在操作风险管理中的解释差距.
结论:
- 拟议的方法为管理水基础设施中的网络风险提供了一个强大的框架.
- 动态可视化和机器学习集成增强了对网络威胁的理解和缓解.
- 该方法支持灵活采用,并提高关键水系统的整体弹性.
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