Neural networks with categorical valued inputs and applications to intrusion detection
Keywords and Phrases
"The main contribution of this study is a neural network intrusion detector, built with a specific goal to minimize false alarms. The obtained performance, 0% false positive rate and very low 0.09% false negative rate, is superior to other published results. Additionally, it has been proven that Hamming distance can be successfully utilized with Gaussian kernels in probabilistic neural networks for categorical valued input data"--Introduction, leaf 1.
Electrical and Computer Engineering
Ph. D. in Computer Engineering
University of Missouri--Rolla
Journal article titles appearing in thesis/dissertation
- Host based intrusion detection using probabilistic neural networks and adaboost
- Probabilistic neural networks with Hamming distance kernels for categorical inputs
ix, 55 leaves
© 2004 Alexander Novokhodko, All rights reserved.
Dissertation - Citation
Library of Congress Subject Headings
Neural networks (Computer science)
Radial basis functions
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu/record=b5393894~S5
Novokhodko, Alexander, "Neural networks with categorical valued inputs and applications to intrusion detection" (2004). Doctoral Dissertations. 1594.
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