Active Anomaly Detection in Heterogeneous Processes
Active Anomaly Detection in Heterogeneous Processes
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DOI：
10.1109/tit.2018.2866257
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发表时间：
2019-04-01
影响因子：
2.5
通讯作者：
Zhao, Qing
中科院分区：
文献类型：
--
作者：
Huang, Boshuang;Cohen, Kobi;Zhao, Qing
研究了异构进程间异常检测的主动推理问题。每次都可以探测进程的一个子集。我们的目标是设计一个顺序的探测策略，动态地确定哪些进程观察在每一个时间和何时终止搜索，使预期的检测时间最小化的约束下，任何进程的错误分类的概率。这个问题福尔斯落入一般设置的序贯设计的实验开创了由Besloff在1959年，其中一个随机化的策略，被称为Besloff测试，提出并证明是渐近最优的错误概率接近零。对于本文考虑的问题，低复杂度的确定性测试被证明具有相同的渐进最优性，同时在有限状态下提供显着更好的性能，并且更快地收敛到最优速率函数，特别是当进程数量较多时。此外，所提出的测试提供了相当大的减少计算复杂性。
An active inference problem of detecting anomalies among heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to terminate the search so that the expected detection time is minimized under a constraint on the probability of misclassifying any process. This problem falls into the general setting of sequential design of experiments pioneered by Chernoff in 1959, in which a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically optimal as the error probability approaches zero. For the problem considered in this paper, a low-complexity deterministic test is shown to enjoy the same asymptotic optimality while offering significantly better performance in the finite regime and faster convergence to the optimal rate function, especially when the number of processes is large. Furthermore, the proposed test offers considerable reduction in computation complexity.