Adaptive Anomaly Detection using Isolation Forest

Abstract

This project developed an adaptive anomaly detection system based on Isolation Forest, applicable to data stream which demands single-scan online algorithms with poly-logarithmic time and space complexities. The proposed system based on Half-Space Tree, an extension of Isolation Forest, is not only capable of detecting anomalies when the underlying concept changes gradually over time, but also capable of detecting abrupt changes in the underlying concepts. Half-Space Trees is significantly better than three existing state-of-the-art distance-based and density-based methods, in terms of detection accuracy, time complexity and memory requirement.

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Document Details

Document Type
Technical Report
Publication Date
Dec 20, 2009
Accession Number
ADA512628

Entities

People

  • Kai Ming Ting

Organizations

  • Monash University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Computer Science
  • Data Mining
  • Data Sets
  • Detection
  • Detectors
  • Information Science
  • Information Systems
  • Kernel Functions
  • Low Density
  • Machine Learning
  • Network Science
  • Standards
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Fluid Dynamics.
  • Neural Network Machine Learning.

Technology Areas

  • Space
  • Space - Space Objects