skip to main content
10.1145/1102256.1102298acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

An evolutionary algorithm to generate ellipsoid network intrusion detectors

Published: 25 June 2005 Publication History

Abstract

This paper introduces the ellipsoid as a geometric structure for detecting network intrusions. Section 2 describes and analyzes the design of the ellipsoid generation algorithm. Experimental design is set forth in Section 3. In Section 4 we analyze experimental results. Section 5 summarizes the paper and provides direction for continued research.

References

[1]
Lincoln Laboratory at Massachusetts Institute of Technology, http://www.ll.mit.edu/IST/ideval/data/. Lincoln Laboratory: DARPA Intrusion Detection Evaluation.
[2]
Don R. Hush Patrick M. Kelly and James M. White. An adaptive algorithm for modifying hyperellipsoidal decision surfaces. Journal of Artificial Neural Networks, 1:49--480, 1994.
[3]
Franco P. Preparata and Michael Ian Shamos. Computational Geometry: An Introduction. Texts and Monographs in Computer Science. Springer-Verlag, 1985.
[4]
Joseph M. Shapiro. An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selection. Master's thesis, Air Force Institute of Technology, Wright Patterson Air Force Base, Ohio, 2005.

Cited By

View all
  • (2018)A negative selection algorithm with online adaptive learning under small samples for anomaly detectionNeurocomputing10.1016/j.neucom.2014.08.022149:PB(515-525)Online publication date: 31-Dec-2018
  • (2018)Negative selection algorithm with constant detectors for anomaly detectionApplied Soft Computing10.1016/j.asoc.2015.08.01136:C(618-632)Online publication date: 27-Dec-2018
  • (2018)An intelligent intrusion detection system for secure wireless communication using IPSO and negative selection classifierCluster Computing10.1007/s10586-017-1643-4Online publication date: 1-Feb-2018

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation
June 2005
431 pages
ISBN:9781450378000
DOI:10.1145/1102256
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2005

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. artificial immune systems
  2. computational geometry
  3. evolutionary computation
  4. negative selection

Qualifiers

  • Article

Conference

GECCO05
Sponsor:

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2018)A negative selection algorithm with online adaptive learning under small samples for anomaly detectionNeurocomputing10.1016/j.neucom.2014.08.022149:PB(515-525)Online publication date: 31-Dec-2018
  • (2018)Negative selection algorithm with constant detectors for anomaly detectionApplied Soft Computing10.1016/j.asoc.2015.08.01136:C(618-632)Online publication date: 27-Dec-2018
  • (2018)An intelligent intrusion detection system for secure wireless communication using IPSO and negative selection classifierCluster Computing10.1007/s10586-017-1643-4Online publication date: 1-Feb-2018

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media