title: Detection and classification of nonlinear dynamic switching events.
abstract: A method is proposed for detecting chaotic switching events. Switching events are classified by the time of the event and by parameter value. Classifications are based on the density of localized dynamics about a test trajectory. This method is shown to be successful in tracking short-time parameter modulation and hyperchaotic key shifting used in otherwise secure communications.
authors.affiliation: Graduate Group in Biophysics, Life Sciences Addition, University of California, Berkeley, California 94720-3200, USA.
authors.forename: Christian
authors.initials: C
authors.lastname: Storm
authors.forename: Walter J
authors.initials: WJ
authors.lastname: Freeman