Article

Analysis and mathematical justification of a fitness function used in an intrusion detection system

Authors:

Pedro A. Diaz-Gomez,

Dean F. HougenAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 1591 - 1592

https://doi.org/10.1145/1068009.1068274

Published: 25 June 2005 Publication History

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Abstract

Convergence to correct solutions in Genetic Algorithms depends largely on the fitness function. A fitness function that captures all goals and constraints can be difficult to find. This paper gives a mathematical justification for a fitness function that has previously been demonstrated experimentally to be effective.

References

[1]

P. Diaz-Gomez and D. Hougen. Improved off-line intrusion detection using a genetic algorithm. To appear in Proceedings of the Seventh International Conference on Enterprise Information Systems, 2005.

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[2]

P. A. Diaz-Gomez and D. F. Hougen. Analysis of an off-line intrusion detection system: A case study in multi-objective genetic algorithms. To appear in the Florida Artificial Intelligence Research Society Conference., 2005.

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[3]

D. Fried and M. Zissman. Intrusion detection evaluation. Technical report, Lincoln Laboratory, MIT, 1998. http://www.ll.mit.edu/IST/ideval/, accessed March 2004.

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[4]

F. M. Ham and I. Kostanic. Principles of Neurocomputing for Science & Engineering. McGraw Hill, 2001.

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[5]

L. Mé. GASSATA, a genetic algorithm as an alternative tool for security audit trail analysis. In First International Workshop on the Recent Advances in Intrusion Detection, Belgium, 1998.

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Cited By

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Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.asoc.2009.06.019
Diaz-Gomez PHougen D(2006)A Genetic Algorithm Approach for Doing Misuse Detection in Audit Trail FilesProceedings of the 15th International Conference on Computing10.1109/CIC.2006.6(329-338)Online publication date: 21-Nov-2006
https://dl.acm.org/doi/10.1109/CIC.2006.6
Karim A(2005)Computational intelligence for network intrusion detectionProceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I10.1007/11596448_25(170-175)Online publication date: 15-Dec-2005
https://dl.acm.org/doi/10.1007/11596448_25

Index Terms

Analysis and mathematical justification of a fitness function used in an intrusion detection system
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Systems security
    1. Operating systems security

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Information

Published In

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2005

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GECCO05

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GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 29, 2005

Washington DC, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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Wu SBanzhaf W(2010)ReviewApplied Soft Computing10.1016/j.asoc.2009.06.01910:1(1-35)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1016/j.asoc.2009.06.019
Diaz-Gomez PHougen D(2006)A Genetic Algorithm Approach for Doing Misuse Detection in Audit Trail FilesProceedings of the 15th International Conference on Computing10.1109/CIC.2006.6(329-338)Online publication date: 21-Nov-2006
https://dl.acm.org/doi/10.1109/CIC.2006.6
Karim A(2005)Computational intelligence for network intrusion detectionProceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I10.1007/11596448_25(170-175)Online publication date: 15-Dec-2005
https://dl.acm.org/doi/10.1007/11596448_25

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Genetic algorithm with variable length chromosomes for network intrusion detection

An experimental study of fitness function and time series forecasting using artificial neural networks

Improving genetic algorithms' efficiency using intelligent fitness functions