Statistical Algorithms for Threat Detection via Sensor Networks

威胁检测(TD)是指对生物、化学或核等有害物质的存在进行评估。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。这些签名是由传感器生成的，这些传感器共同组成了一个网络系统。这类系统的体系结构可以是串联、并行或分层的。这类签名的一个关键特征是它们往往不准确、不完整和不可靠。此外，传感器可以是合作的，也可以是对抗性的，后者是由于破坏和心理手段。首席研究员和他的同事们建议阐明TD情景的数学基础，以便以有原则的方式整合来自多个传感器的签名。目标是用数字概率来表示威胁的存在。这相当于集成了通过分布式网络结构过滤的签名，并被不精确、伪装和协商所污染。作为概率统计的一个研究课题，对受污染和伪装的签名进行整合是一个新的课题。贝叶斯方法和经典方法，以及两者的巧妙组合，都将被引用。这项工作的关键将需要开发有意义的可能性函数，以捕捉生理和心理问题的本质。目前情报和国家安全方面的做法是用语言和定性术语表达威胁，如可能、可能、可能等。这些表达是不可操作的。这项研究将把威胁检测情景放在一个概率框架中，以便采取果断行动来缓解威胁。这项工作将在石油勘探、天气预报、医疗诊断和社会文化建模等民用应用中产生更广泛的影响。

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.