A Neural Network Anomaly Detector Using the Random Cluster Model

TL;DR

This paper introduces a neural network-based anomaly detection method that leverages the random cluster model to define bounds on classification distances, enabling effective identification of anomalies in data.

Contribution

It combines the random cluster model with neural networks to improve anomaly detection, providing conditions for class and individual anomaly identification.

Findings

Defined an upper bound on classification distance using the random cluster model

Developed a neural network model for decision boundary representation

Demonstrated effective anomaly detection in classification tasks

Abstract

The random cluster model is used to define an upper bound on a distance measure as a function of the number of data points to be classified and the expected value of the number of classes to form in a hybrid K-means and regression classification methodology, with the intent of detecting anomalies. Conditions are given for the identification of classes which contain anomalies and individual anomalies within identified classes. A neural network model describes the decision region-separating surface for offline storage and recall in any new anomaly detection.