Variable Discretisation for Anomaly Detection using Bayesian Networks
Jonathan Legg · 2017
Abstract : Anomaly detection is the process by which low probability events are automatically found against a background of normal activity.By definition there must be many more normal events than anomalous ones. This rare nature of anomalies causes numerical problemsfor probabilistic methods designed to automatically detect them. This report describes an algorithm that introduces new discretisationlevels to support the representation of low probability values in the context of Bayesian network anomaly detection. It is an engineeringsolution to a problem with an extant discretisation tool that represents a data sets fine structure but fails to capture extreme values ornulls between modes in its probability density. It is demonstrated that the limitations of the extant tool can be overcome using examplesof integer and continuous data.