  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.
