Technical Program

Paper Detail

Paper Title Sequential anomaly detection with observation control
Paper IdentifierTH4.R6.1
Authors Aristomenis Tsopelakos, Georgios Fellouris, Venugopal V. Veeravalli, University of Illinois at Urbana Champaign, United States
Session Quickest Change Detection II
Location Sorbonne, Level 5
Session Time Thursday, 11 July, 16:40 - 18:00
Presentation Time Thursday, 11 July, 16:40 - 17:00
Manuscript  Click here to download the manuscript
Abstract The problem of anomaly detection is considered when a number of processes are observed sequentially, but it is possible to sample only a subset of them at a time according to an adaptive control (sampling) policy. The problem is to stop sampling as soon as possible and identify the anomalous process while controlling appropriate error probabilities. We consider two versions of this problem: in the first one there is no assumption regarding the anomalous processes, in the second their number is assumed to be known a priori. For each version, we characterize the optimal asymptotic performance as the error probabilities vanish, as well as the sampling rules that lead to asymptotic optimality. Moreover, we present two examples of such sampling rules for each setup which vary dramatically in terms of their computational complexity.