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Authors: Pedro A. Diaz-Gomez and Dean F. Hougen

Affiliation: Robotics, Evolution, Adaptation, and Learning Laboratory (REAL Lab), School of Computer Science, University of Oklahoma, United States

ISBN: 978-972-8865-89-4

Keyword(s): Misuse detection, genetic algorithms, neural networks, false negative, false positive.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence and Decision Support Systems ; Enterprise Information Systems ; Evolutionary Programming

Abstract: Misuse detection can be addressed as an optimization problem, where the problem is to find an array of possible intrusions x that maximizes a function f (·) subject to a constraint r imposed by a user’s actions performed on a computer. This position paper presents and compares two ways of finding x, in audit data, by using neural networks and genetic algorithms.

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Paper citation in several formats:
A. Diaz-Gomez P.; F. Hougen D. and (2007). MISUSE DETECTION - A Neural Network vs. A Genetic Algorithm Approach.In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-972-8865-89-4, pages 459-462. DOI: 10.5220/0002410904590462

@conference{iceis07,
author={Pedro {A. Diaz{-}Gomez} and Dean {F. Hougen}},
title={MISUSE DETECTION - A Neural Network vs. A Genetic Algorithm Approach},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2007},
pages={459-462},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002410904590462},
isbn={978-972-8865-89-4},
}

TY - CONF

JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - MISUSE DETECTION - A Neural Network vs. A Genetic Algorithm Approach
SN - 978-972-8865-89-4
AU - A. Diaz-Gomez, P.
AU - F. Hougen, D.
PY - 2007
SP - 459
EP - 462
DO - 10.5220/0002410904590462

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