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.
@article{arxiv.1704.05741,
title = {Study of Anomaly Detection Based on Randomized Subspace Methods in IP Networks},
author = {M. Kaloorazi and R. C. de Lamare},
journal= {arXiv preprint arXiv:1704.05741},
year = {2017}
}