快速检测网络中不断增长的动态异常
Quickest Detection of Growing Dynamic Anomalies in Networks
摘要：
研究了传感器网络中最快增长动态异常检测问题。最初，在传感器的观察，这是由决策者顺序采样，根据变化前的分布生成。在某个未知但确定的时刻，网络中出现动态异常，随着时间的推移影响不同的传感器集合。受影响传感器的观测值由变更后分布生成。它是假设受影响的传感器的数量随着时间的推移而增加，并且只有初始和最终的异常的大小是已知的决策者。目标是尽可能快地检测异常的出现，同时保证足够低的误报警事件频率。这个检测问题是作为一个随机最优化问题，通过使用的延迟度量，是基于最坏的可能路径的异常。提出了一个检测规则，是渐近最优的平均时间到虚警趋于无穷大。最后，数值结果验证了我们的理论分析。
英文摘要：
The problem of quickest growing dynamic anomaly detection in sensor networks is studied. Initially, the observations at the sensors, which are sampled sequentially by the decision maker, are generated according to a pre-change distribution. At some unknown but deterministic time instant, a dynamic anomaly emerges in the network, affecting a different set of sensors as time progresses. The observations of the affected sensors are generated from a post-change distribution. It is assumed that the number of affected sensors increases with time, and that only the initial and the final size of the anomaly are known by the decision maker. The goal is to detect the emergence of the anomaly as quickly as possible while guaranteeing a sufficiently low frequency of false alarm events. This detection problem is posed as a stochastic optimization problem by using a delay metric that is based on the worst possible path of the anomaly. A detection rule is proposed that is asymptotically optimal as the mean time to false alarm goes to infinity. Finally, numerical results are provided to validate our theoretical analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
