本powerpoint演示首先简要概述了市政饮用水和废水系统的中断情况。这项研究的目的是估计 故意破坏将在一个平台上执行 一定时期内的市政供水系统。方法包括:数据融合- 将多源、多格式数据结合起来，以支持 及时解释和有效利用信息； 信息- 情报（监视、金融、网络）和 操作（静态、动态）；和 模拟- 检测并可视化潜在威胁，以及 生成用于统计分析的数据。举例说明了一个为期两周的模拟周期，涉及1000多个合成项目 一个市政供水系统的情报数据项，以及20个嵌入合成材料的“盲”威胁 情报数据。演示摘要包括以下几点: 基于仿真的数据融合可以有效地检测 城市饮用水的潜在安全威胁 和废水资产； 盲实验结果表明，检测率达到90%以上 在大型计算机中嵌入合成威胁的准确性 大量情报和作战数据；事后分析评估潜在风险的可能性 基于用户定义的威胁标准的威胁；和 数据融合平台可以提供及时的决策 支持水安全信息合作。

This powerpoint presentation begins by providing a brief overview of disruptions to municipal drinking water and wastewater systems. The study objective was to estimate the likelihood that an act of intentional disruption would be executed on a municipal water system in a given period. The approach included: data fusion - combine multisource, multiformat data to support timely interpretation and efficient use of information; information - intelligence (surveillance, financial, cyber), and operations (static, dynamic); and, simulation - detect and visualize potential threats, and generate data for statistical analyses. An example is presented of a 2-week simulation period involving 1,000+ synthetic intelligence data items at a municipal water system, and 20 composed "blind" threats embedded with synthetic intelligence data. Presentation summary included the following points: simulation-based data fusion efficiently detects potential security threats to municipal drinking water and wastewater assets; results of blind experiment show over 90% detection accuracy for composed threats embedded within large volumes of intelligence and operational data; post-hoc analysis estimates the likelihood of potential threats based on user-defined threat criteria; and, data fusion platform can provide timely decision support for water security-information collaboratives.