# Study of Anomaly Detection Based on Randomized Subspace Methods in IP Networks

## TL;DR

This paper introduces randomized subspace methods for anomaly detection in IP networks, improving robustness and detection accuracy over traditional PCA-based techniques through a novel matrix decomposition approach.

## Contribution

It presents a new randomized subspace approach for network anomaly detection that enhances robustness and detection performance compared to existing PCA-based methods.

## Key findings

- Improved detection rate over PCA-based methods
- Enhanced robustness to noise in network traffic analysis
- Effective anomaly detection in IP networks using randomized subspace techniques

## Abstract

In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plus-anomalous matrix decomposition aided by random subspace techniques and subsequently detect traffic anomalies in the anomalous subspace using a statistical test. Experimental results demonstrate improvement over the traditional principal component analysis-based subspace methods in terms of robustness to noise and detection rate.