A Kernel-Based Nonparametric Test for Anomaly Detection over Line Networks

TL;DR

This paper introduces a kernel-based nonparametric test for detecting anomalous intervals in line networks using mean embeddings and MMD, with proven asymptotic success and efficient algorithms.

Contribution

It develops a novel nonparametric test based on RKHS embeddings for anomaly detection in line networks, with theoretical guarantees and computational efficiency.

Findings

Test is asymptotically successful for intervals longer than O(log n)

Proposed algorithm reduces computational complexity significantly

Numerical results confirm theoretical predictions

Abstract

The nonparametric problem of detecting existence of an anomalous interval over a one dimensional line network is studied. Nodes corresponding to an anomalous interval (if exists) receive samples generated by a distribution q, which is different from the distribution p that generates samples for other nodes. If anomalous interval does not exist, then all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary, and are unknown. In order to detect whether an anomalous interval exists, a test is built based on mean embeddings of distributions into a reproducing kernel Hilbert space (RKHS) and the metric of maximummean discrepancy (MMD). It is shown that as the network size n goes to infinity, if the minimum length of candidate anomalous intervals is larger than a threshold which has the order O(log n), the proposed test is asymptotically successful, i.e., the probability of detection error approaches zero asymptotically. An efficient algorithm to perform the test with substantial computational complexity reduction is proposed, and is shown to be asymptotically successful if the condition on the minimum length of candidate anomalous interval is satisfied. Numerical results are provided, which are consistent with the theoretical results.