Quickest Detection of Growing Dynamic Anomalies in Networks

TL;DR

This paper addresses the challenge of rapidly detecting growing anomalies in sensor networks by developing an asymptotically optimal detection rule that minimizes delay while controlling false alarms.

Contribution

It introduces a novel detection method for dynamic anomalies with unknown growth patterns, proven to be asymptotically optimal under certain conditions.

Findings

Proposed detection rule is asymptotically optimal as false alarm rate increases.

Numerical results validate theoretical delay and false alarm guarantees.

Method effectively detects anomalies with unknown growth over time.

Abstract

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.