Hashing for Structure-based Anomaly Detection

TL;DR

This paper introduces a hashing-based anomaly detection method that embeds data into a high-dimensional Preference Space, enabling efficient identification of outliers as isolated points with state-of-the-art accuracy.

Contribution

It proposes a novel isolation-based anomaly detection technique using Locality Sensitive Hashing in Preference Space, improving efficiency and performance over existing methods.

Findings

Achieves state-of-the-art anomaly detection performance

Reduces computational cost compared to traditional methods

Effectively identifies structured outliers in high-dimensional data

Abstract

We focus on the problem of identifying samples in a set that do not conform to structured patterns represented by low-dimensional manifolds. An effective way to solve this problem is to embed data in a high dimensional space, called Preference Space, where anomalies can be identified as the most isolated points. In this work, we employ Locality Sensitive Hashing to avoid explicit computation of distances in high dimensions and thus improve Anomaly Detection efficiency. Specifically, we present an isolation-based anomaly detection technique designed to work in the Preference Space which achieves state-of-the-art performance at a lower computational cost. Code is publicly available at https://github.com/ineveLoppiliF/Hashing-for-Structure-based-Anomaly-Detection.