Online Anomaly Detection via Class-Imbalance Learning

TL;DR

This paper introduces a novel online learning algorithm for anomaly detection that maximizes the Gmean metric, effectively handling class imbalance in real-time applications, and demonstrates competitive performance with efficient computation.

Contribution

It proposes a new online learning algorithm that maximizes Gmean for class-imbalance anomaly detection, with proven convex surrogate loss and extensive experimental validation.

Findings

Performance comparable to Cost-Sensitive Online Classification (CSOC) algorithms.

Maintains close to perception-level running time.

Exhibits consistent performance across various dataset sizes.

Abstract

Anomaly detection is an important task in many real world applications such as fraud detection, suspicious activity detection, health care monitoring etc. In this paper, we tackle this problem from supervised learning perspective in online learning setting. We maximize well known \emph{Gmean} metric for class-imbalance learning in online learning framework. Specifically, we show that maximizing \emph{Gmean} is equivalent to minimizing a convex surrogate loss function and based on that we propose novel online learning algorithm for anomaly detection. We then show, by extensive experiments, that the performance of the proposed algorithm with respect to $sum$ metric is as good as a recently proposed Cost-Sensitive Online Classification(CSOC) algorithm for class-imbalance learning over various benchmarked data sets while keeping running time close to the perception algorithm. Our another conclusion is that other competitive online algorithms do not perform consistently over data sets of varying size. This shows the potential applicability of our proposed approach.