Dimensionality reduction for denial of service detection problems using RBFNN output sensitivity

W. W Y Ng, Kow Chuen Chang, Daniel S. Yeung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

25 Citations (Scopus)

Abstract

In this paper, we have presented a feature importance ranking methodology based on the stochastic radial basis function neural network output sensitivity measure and have shown, for the 10% training set of the DARPA network intrusion detection data set prepared by MIT Lincoln Labs, that 33 out of 41 features (more than 80% dimensionality reduction) can be removed without causing great harm to the classification accuracy of denial of service (DoS) attacks and normal packets (false positives rise from 0.7% to 0.93%). The reduced feature subset leads to more generalized and less complex model for classifying DoS and Normal. Exploratory discussions on the relevancy of the selected features and the DoS attack types are presented.
Original languageEnglish
Title of host publicationInternational Conference on Machine Learning and Cybernetics
Pages1293-1298
Number of pages6
Volume2
Publication statusPublished - 1 Dec 2003
EventInternational Conference on Machine Learning and Cybernetics - Xi'an, China
Duration: 2 Nov 20035 Nov 2003

Conference

ConferenceInternational Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityXi'an
Period2/11/035/11/03

Keywords

  • Denial of Service (DoS)
  • Feature Selection
  • Network Intrusion Detection
  • RBFNN
  • Sensitivity Analysis

ASJC Scopus subject areas

  • Artificial Intelligence

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