Statistical Algorithms for Threat Detection via Sensor Networks

通过传感器网络进行威胁检测的统计算法

• 批准号: 0915156
• 负责人: Nozer Singpurwalla
• 金额: $ 71.62万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915156&HistoricalAwards=false
• 关键词: Statistical; Algorithms; Threat; Detection; via

Threat detection (TD) means an assessment of the presence of harmful agents, biological, chemical, or nuclear. The footprint (or signatures) of such agents could be qualitative and verbal, and/or quantitative. These signatures are generated by sensors which collectively form a networked system. The architecture of such systems could be series, parallel, or hierarchical. A key feature of such signatures is that they tend to be imprecise, incomplete, and unreliable. Furthermore, the sensors could be co-operative or adversarial, the latter due to sabotage and psychological ploys. The principal investigator and his colleagues propose to articulate the mathematical underpinnings of the TD scenario, in order to integrate signatures from a multitude of sensors in a principled way. The goal is to express the presence of threats in terms of numerical probabilities. This tantamounts to integrating signatures which are filtered via distributed network structures, and are contaminated by imprecision, camouflage, and parleying. As a research topic in probability and statistics, the matter of integrating contaminated and camouflaged signatures is new. Both Bayesian and classical methods, as well as a cunning combination of the two, will be invoked. The crux of the work will entail developing meaningful likelihood functions that capture the essence of the physical and psychological issues.Current practice in intelligence and national security is to express threat in verbal and qualitative terms like possible, probable, likely, etc. Such expressions are not actionable. This research will place the threat detection scenario in a probabilistic framework so that decisive actions to mitigate threats can be taken. The work will have broader impacts in civilian applications such as oil exploration, weather prediction, medical diagnosis, and socio-cultural modeling.

威胁检测（TD）是指对生物、化学或核有害物剂存在的评估。 这些代理人的足迹（或签名）可以是定性的和口头的，和/或定量的。 这些签名由共同形成网络系统的传感器生成。 这种系统的架构可以是串联的、并联的或分层的。 这种签名的一个关键特征是它们往往是不精确、不完整和不可靠的。 此外，传感器可以是合作的或对抗的，后者是由于破坏和心理策略。 首席研究员和他的同事们建议阐明TD场景的数学基础，以便以原则性的方式整合来自众多传感器的签名。 目标是用数字概率来表示威胁的存在。 这种方法相当于整合通过分布式网络结构过滤的签名，并且被不精确，伪装和parleying污染。 作为概率统计领域的一个研究课题，污染签名与非污染签名的融合是一个新的课题。 贝叶斯和经典方法，以及两者的巧妙组合，将被调用。 这项工作的关键将需要开发有意义的可能性函数，捕捉的本质的身体和心理问题。目前的做法，在情报和国家安全是表达威胁的口头和定性的条件，如可能的，可能的，很可能的，等等，这样的表达是不可诉的。 这项研究将把威胁检测方案在概率框架，以便采取果断行动，以减轻威胁。 这项工作将在民用应用中产生更广泛的影响，如石油勘探，天气预报，医疗诊断和社会文化建模。