Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10387
Title: Dimensionality reduction for denial of service detection problems using RBFNN output sensitivity
Authors: Ng, WWY
Chang, RKC 
Yeung, DS
Issue Date: 2003
Source: 2003 International Conference on Machine Learning and Cybernetics, 2-5 November 2003, v. 2, p. 1293-1298
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.
Keywords: Computer networks
Radial basis function networks
Security of data
Statistical analysis
Publisher: IEEE
ISBN: 0-7803-8131-9
DOI: 10.1109/ICMLC.2003.1259688
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

WEB OF SCIENCETM
Citations

6
Last Week
0
Last month
0
Citations as of Sep 29, 2020

Page view(s)

142
Last Week
0
Last month
Citations as of Sep 29, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.