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RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection.

Ke Wu¹, Kun Zhang¹, Wei Fan²

¹Department of Computer Science, Xavier University of Louisiana.

Proceedings. IEEE International Conference on Data Mining

|February 17, 2015

Summary

This study introduces a new semi-supervised algorithm for anomaly detection in streaming data using a randomized space forest (RS-Forest). The method offers high detection rates and fast responses for evolving data streams.

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Area of Science:

Computer Science
Data Mining
Machine Learning

Background:

Anomaly detection in streaming data is crucial for many applications.
Existing methods struggle with the dynamic nature of data streams.

Purpose of the Study:

To propose a novel one-class semi-supervised algorithm for anomaly detection in streaming data.
To address the challenges posed by evolving data streams.

Main Methods:

Developed a fast and accurate density estimator using multiple randomized space trees (RS-Trees) forming an RS-Forest.
Integrated statistical attribute range estimation and dual node profiles for efficient model updates.
Derived theoretical upper bounds and analyzed asymptotic properties via bias-variance decomposition.

Main Results:

The proposed RS-Forest algorithm demonstrated high anomaly detection rates.
The method exhibited fast response times, crucial for real-time data streams.
The algorithm showed insensitivity to most parameter settings, enhancing usability.

Conclusions:

The novel RS-Forest algorithm effectively detects anomalies in streaming data.
The approach is robust and adaptable to evolving data stream characteristics.
This method offers a significant advancement over current state-of-the-art techniques.