Nonparametric Detection of Anomalous Data Streams

TL;DR

This paper develops a nonparametric method using maximum mean discrepancy for detecting anomalous data streams without prior knowledge of distributions, establishing conditions for exponential consistency and optimality.

Contribution

It introduces a distribution-free test for anomaly detection in data streams that is proven to be order-level optimal under certain conditions.

Findings

The proposed test is exponentially consistent when sample size exceeds a constant times log n.

The test performs better or comparable to existing methods in numerical experiments.

Optimality bounds are established for the sample size needed for reliable detection.

Abstract

A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n sequences with s anomalous sequences to be detected. Each typical sequence contains m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence contains m i.i.d. samples drawn from a distribution q that is distinct from p. The distributions p and q are assumed to be unknown in advance. Distribution-free tests are constructed using maximum mean discrepancy as the metric, which is based on mean embeddings of distributions into a reproducing kernel Hilbert space. The probability of error is bounded as a function of the sample size m, the number s of anomalous sequences and the number n of sequences. It is then shown that with s known, the constructed test is exponentially consistent if m is greater than a constant factor of log n, for any p and q, whereas with s unknown, m should has an order strictly greater than log n. Furthermore, it is shown that no test can be consistent for arbitrary p and q if m is less than a constant factor of log n, thus the order-level optimality of the proposed test is established. Numerical results are provided to demonstrate that our tests outperform (or perform as well as) the tests based on other competitive approaches under various cases.