1 research outputs found
Asymptotically Optimal Anomaly Detection via Sequential Testing
Sequential detection of independent anomalous processes among K processes is
considered. At each time, only M processes can be observed, and the
observations from each chosen process follow two different distributions,
depending on whether the process is normal or abnormal. Each anomalous process
incurs a cost per unit time until its anomaly is identified and fixed.
Switching across processes and state declarations are allowed at all times,
while decisions are based on all past observations and actions. The objective
is a sequential search strategy that minimizes the total expected cost incurred
by all the processes during the detection process under reliability
constraints. Low-complexity algorithms are established to achieve
asymptotically optimal performance as the error constraints approach zero.
Simulation results demonstrate strong performance in the finite regime.Comment: 28 pages, 5 figures, part of this work will be presented at the 52nd
Annual Allerton Conference on Communication, Control, and Computing, 201