Variable Discretisation for Anomaly Detection using Bayesian Networks

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 problems for probabilistic methods designed to automatically detect them. This report describes an algorithm that introduces new discretisation levels 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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2017
Accession Number
AD1027342

Entities

People

  • Jonathan Legg

Organizations

  • Defence Science and Technology Group

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automatic Identification Systems
  • Bayesian Networks
  • Change Detection
  • Data Mining
  • Detection
  • Identification
  • Identification Systems
  • Information Science
  • Machine Learning
  • National Security
  • Probability
  • Security
  • Training

Fields of Study

  • Computer science

Readers

  • Statistical inference.
  • Superconducting Magnet Technology
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference