Numerical Analysis for Relevant Features in Intrusion Detection (NARFid)

Abstract

This thesis evaluates the usefulness of good feature subsets for the general classification task of identifying cyber attacks and network services. The generality of the selected features elucidates the relevance or irrelevance for the classification task of intrusion detection. Additionally, the work provides an extension to assessing features by inter-class separability(Bhattacharyya Coefficient) for multiple class problems, which intends to select the best-performing features for all of the classes.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2009
Accession Number
ADA499600

Entities

People

  • Jose A. Gonzalez

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Cyber
  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Computer Networks
  • Data Science
  • Databases
  • Department Of Defense
  • Detectors
  • Experimental Design
  • Information Processing
  • Information Science
  • Information Systems
  • Intrusion Detectors
  • Knowledge Management
  • Machine Learning
  • Network Protocols
  • Probabilistic Models
  • Supervised Machine Learning
  • Surveys

Fields of Study

  • Computer science

Readers

  • Calculus or Mathematical Analysis
  • Cybersecurity.
  • Neural Network Machine Learning.

Technology Areas

  • Cyber