Machine learning and user interface for cyber risk management of water infrastructure
Machine learning and user interface for cyber risk management of water infrastructure
复制标题
用于水利基础设施网络风险管理的机器学习和用户界面
DOI：
10.1111/risa.14209
复制
发表时间：
2023
期刊：
影响因子：
3.8
通讯作者：
Behara, Ravi
中科院分区：
文献类型：
--
作者：
Neshenko, Nataliia;Bou‐Harb, Elias;Furht, Borko;Behara, Ravi
随着水厂的不断现代化，对其进行网络攻击的风险可能危及公众健康以及水处理和分配的经济效率。这篇文章表明，在不断变化的威胁环境下，开发改进的技术以支持关键水基础设施的网络风险管理的重要性。特别是，我们提出了一种方法，它独特地结合了机器学习、信任函数理论、作战性能度量和动态可视化，为攻击推理、定位和影响估计提供了所需的粒度。我们说明了对视觉领域感知异常探索的关注如何导致性能改进、更精确的异常定位和有效的风险优先级。提出的方法元素可以独立使用，支持各种异常检测方法的探索。因此，它可以通过提供丰富的背景信息和弥合解释差距，促进操作风险的有效管理。
With the continuous modernization of water plants, the risk of cyberattacks on them potentially endangers public health and the economic efficiency of water treatment and distribution. This article signifies the importance of developing improved techniques to support cyber risk management for critical water infrastructure, given an evolving threat environment. In particular, we propose a method that uniquely combines machine learning, the theory of belief functions, operational performance metrics, and dynamic visualization to provide the required granularity for attack inference, localization, and impact estimation. We illustrate how the focus on visual domain‐aware anomaly exploration leads to performance improvement, more precise anomaly localization, and effective risk prioritization. Proposed elements of the method can be used independently, supporting the exploration of various anomaly detection methods. It thus can facilitate the effective management of operational risk by providing rich context information and bridging the interpretation gap.