Active Anomaly Detection in Heterogeneous Processes

Boshuang Huang, Kobi Cohen, Qing Zhao

פרסום מחקרי: פרסום בכתב עתמאמרביקורת עמיתים

תקציר

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.

שפה מקוריתאנגלית אמריקאית
מספר המאמר8443436
עמודים (מ-עד)2284-2301
מספר עמודים18
כתב עתIEEE Transactions on Information Theory
כרך65
מספר גיליון4
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 1 אפר׳ 2019

ASJC Scopus subject areas

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