Doctoral Dissertations

Title

Neural networks with categorical valued inputs and applications to intrusion detection

Keywords and Phrases

AdaBoost

Abstract

"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.

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Publisher

University of Missouri--Rolla

Publication Date

Fall 2004

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

Pagination

ix, 55 leaves

Note about bibliography

Includes bibliographical references.

Rights

© 2004 Alexander Novokhodko, All rights reserved.

Document Type

Dissertation - Citation

File Type

text

Language

English

Library of Congress Subject Headings

Computer security
Neural networks (Computer science)
Radial basis functions

Thesis Number

T 8645

Print OCLC #

62253239

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

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