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
Keywords: Computer networks
Radial basis function networks
Security of data
Statistical analysis
Issue Date: 2003
Publisher: IEEE
Source: 2003 International Conference on Machine Learning and Cybernetics, 2-5 November 2003, v. 2, p. 1293-1298 How to cite?
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.
URI: http://hdl.handle.net/10397/10387
ISBN: 0-7803-8131-9
DOI: 10.1109/ICMLC.2003.1259688
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