White Functionals for Anomaly Detection in Dynamical Systems

TITLE: White Functionals for Anomaly Detection in Dynamical Systems.

AUTHORS: Marco Cuturi, Jean-Philippe Vert, Alexandre d'Aspremont

ABSTRACT: We propose new methodologies to detect anomalies in discrete-time processes taking values in a set Z. The method is based on the inference of functionals whose evaluations on successive states visited by the process have low autocorrelations. Deviations from this behavior are used to flag anomalies. The candidate functionals are estimated in a subset of a reproducing kernel Hilbert space associated with Z. We provide experimental results which show that these techniques compare favorably with other algorithms.

STATUS: NIPS 2009.

ArXiv PREPRINT: 0908.0137

PAPER: White Functionals for Anomaly Detection in Dynamical Systems in pdf