A comparative study of real-valued negative selection to statistical anomaly detection techniques

Thomas Stibor*, Jonathan Timmis, Claudia Eckert

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

114 Dyfyniadau (Scopus)

Crynodeb

The (randomized) real-valued negative selection algorithm is an anomaly detection approach, inspired by the negative selection immune system principle. The algorithm was proposed to overcome scaling problems inherent in the hamming shape-space negative selection algorithm. In this paper, we investigate termination behavior of the real-valued negative selection algorithm with variable-sized detectors on an artificial data set. We then undertake an analysis and comparison of the classification performance on the high-dimensional KDD data set of the real-valued negative selection, a real-valued positive selection and statistical anomaly detection techniques. Results reveal that in terms of detection rate, real-valued negative selection with variable-sized detectors is not competitive to statistical anomaly detection techniques on the KDD data set. In addition, we suggest that the termination guarantee of the real-valued negative selection with variable-sized detectors is very sensitive to several parameters.

Iaith wreiddiolSaesneg
TeitlArtificial Immune Systems
Is-deitl4th International Conference, ICARIS 2005, Banff, Alberta, Canada, August 14-17, 2005, Proceedings
CyhoeddwrSpringer Nature
Tudalennau262-275
Nifer y tudalennau14
Cyfrol3627
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2005
Digwyddiad4th International Conference on Artificial Immune Systems, ICARIS 2005 - Banff, Alta., Canada
Hyd: 14 Awst 200517 Awst 2005

Cyfres gyhoeddiadau

EnwLecture Notes in Computer Science
CyhoeddwrSpringer Nature
ISSN (Argraffiad)0302-9743

Cynhadledd

Cynhadledd4th International Conference on Artificial Immune Systems, ICARIS 2005
Gwlad/TiriogaethCanada
DinasBanff, Alta.
Cyfnod14 Awst 200517 Awst 2005

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