Network Intrusion Dataset Assessment

Abstract

Research into classification using Anomaly Detection (AD) within the field of Network Intrusion Detection (NID), or Network Intrusion Anomaly Detection (NIAD), is common, but operational use of the classifiers discovered by research is not. One reason for the lack of operational use is most published testing of AD methods uses artificial datasets: making it difficult to determine how well published results apply to other datasets and the networks they represent. This research develops a method to predict the accuracy of an AD-based classifier when applied to a new dataset, based on the di erence between an already classified dataset and the new dataset. The resulting method does not accurately predict classifier accuracy, but does allow some information to be gained regarding the possible range of accuracy. Further refinement of this method could allow rapid operational application of new techniques within the NIAD field, and quick selection of the classifier(s) that will be most accurate for the network.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2013
Accession Number
ADA582660

Entities

People

  • David J. Weller-fahy

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Communication Systems
  • Computational Science
  • Computer Languages
  • Computer Networks
  • Data Mining
  • Databases
  • Denial Of Service Attack
  • Information Processing
  • Information Science
  • Intrusion Detectors
  • Machine Learning
  • Network Science
  • Ontologies
  • Predictive Modeling
  • Supervised Machine Learning
  • Surveys

Fields of Study

  • Computer science

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Neural Network Machine Learning.