ATD: Statistical Methods for Threat Detection

对当前大量可用数据进行成功的统计分析，可以成功地实现早期威胁检测。本提案由两部分组成。第一部分着重于病原体样本中突变的检测。许多新出现的健康威胁是由于不断进化的病原体种群中的新突变造成的，现在可以使用大规模平行测序实验对其进行分析。研究人员与斯坦福基因组技术中心（Stanford Genome Technology Center）池汉利博士（Hanlee Ji）的实验室合作，该实验室的深度测序平台可以检测病原体样本中低流行率的突变。以前主要是从算法的角度来处理这个问题，缺乏误差估计的统计模型。研究者提出了单核苷酸变化和一般结构变异的分析方法，并考虑了单样本分析、多样本同时分析和匹配样本比较。该提案的第二部分考虑了更一般框架下的威胁检测：在一个或多个并行数据流中检测来自背景条件的变化。例如，对计算机网络的网络攻击，将好战因子（如地雷、飞机）引入以前安静的环境，有毒化学品的出现，病毒或细菌的基因修饰等。当感兴趣的信号可能仅在一小部分源中出现时，其主要贡献是集成来自大量分布式源的数据的一般概念框架。这一建议推动了变点检测、混合估计、经验贝叶斯估计和错误发现率控制等领域的理论发展。对现代科学和技术活动中收集的大量数据进行成功的统计分析，可以导致成功的早期威胁检测。本提案由两部分组成。第一部分着重于病原体样本中突变的检测。许多新出现的健康威胁是由于不断进化的病原体种群中的新突变造成的，现在可以使用下一代测序实验对其进行分析。新突变的准确检测很重要，因为它们可能赋予携带它的病毒生存优势。目前，这个问题主要是从算法的角度来处理，缺乏误差估计的统计模型。本提案中制定的方法将弥补这一差距。该提案的第二部分考虑了更一般框架下的威胁检测：在一个或多个并行数据流中检测来自背景条件的变化。这方面的例子包括对计算机网络的网络攻击，将交战物（如地雷、飞机）引入以前平静的环境，出现有毒化学品，对病毒或细菌进行基因改造等。当感兴趣的信号可能仅在一小部分源中出现时，其主要贡献是集成来自潜在大量分布式源的数据的一般概念框架。

Successful statistical analysis of the massive amounts of data available today can lead to successful, early threat detection. This proposal consists of two parts. The first part focuses on the detection of mutations in pathogen samples. Many emerging health threats are due to new mutations in evolving pathogen populations, which can now be profiled using massively parallel sequencing experiments. The investigators work with Dr. Hanlee Ji's laboratory in the Stanford Genome Technology Center, whose deep sequencing platform allows the detection of low prevalence mutations in pathogen samples. This problem was previously treated mainly from an algorithmic perspective, lacking statistical models for error estimates. The investigators propose methods for analysis of single nucleotide changes and general structural variants, and consider the analysis of single samples, the simultaneous analysis of multiple samples, and the comparison of matched samples. The second part of the proposal considers threat detection in a more general framework: detection of changes from background condition in one or more parallel streams of data. Examples are cyber-attacks on computer networks, introduction of belligerent agents (e.g. landmines, aircraft) into previously quiescent environments, appearance of noxious chemicals, genetic modifications of viruses or bacteria, etc. The main contribution is a general conceptual framework for integrating data from a large number of distributed sources, when the signal of interest may be present in only a small fraction of the sources. This proposal motivates theoretical developments in the areas of change-point detection, mixture estimation, empirical Bayes estimation, and false discovery rate control. Successful statistical analysis of the massive amounts of data collected in modern scientific and technological activities can lead to successful, early threat detection. This proposal consists of two parts. The first part focuses on the detection of mutations in pathogen samples. Many emerging health threats are due to new mutations in evolving pathogen populations, which can now be profiled using next generation sequencing experiments. The accurate detection of new mutations is important, because they may confer survival advantage to the virus that carries it. Currently, this problem has been treated mainly from an algorithmic perspective, lacking statistical models for error estimates. The methods developed in this proposal will bridge this gap. The second part of the proposal considers threat detection in a more general framework: detection of changes from background condition in one or more parallel streams of data. Examples include cyber-attacks on computer networks, introduction of belligerent agents (e.g. landmines, aircraft) into previously quiescent environments, appearance of noxious chemicals, genetic modifications of viruses or bacteria, etc. The main contribution is a general conceptual framework for integrating data from a potentially large number of distributed sources, when the signal of interest may be present in only a small fraction of the sources.