Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples

TL;DR

This paper addresses the challenge of quickest anomaly detection in sensor networks with unlabeled data, proposing algorithms that achieve near-optimal detection delays despite the lack of label information.

Contribution

It introduces novel algorithms based on mixture likelihood ratios and maximum likelihood estimates for unlabeled data, establishing their theoretical optimality and practical effectiveness.

Findings

Algorithms achieve asymptotic optimality in detection delay.

Theoretical bounds match the performance of proposed methods.

Effective detection in both static and dynamic anomaly scenarios.

Abstract

The problem of quickest anomaly detection in networks with unlabeled samples is studied. At some unknown time, an anomaly emerges in the network and changes the data-generating distribution of some unknown sensor. The data vector received by the fusion center at each time step undergoes some unknown and arbitrary permutation of its entries (unlabeled samples). The goal of the fusion center is to detect the anomaly with minimal detection delay subject to false alarm constraints. With unlabeled samples, existing approaches that combines local cumulative sum (CuSum) statistics cannot be used anymore. Several major questions include whether detection is still possible without the label information, if so, what is the fundamental limit and how to achieve that. Two cases with static and dynamic anomaly are investigated, where the sensor affected by the anomaly may or may not change with time. For the two cases, practical algorithms based on the ideas of mixture likelihood ratio and/or maximum likelihood estimate are constructed. Their average detection delays and false alarm rates are theoretically characterized. Universal lower bounds on the average detection delay for a given false alarm rate are also derived, which further demonstrate the asymptotic optimality of the two algorithms.