Article

Is negative selection appropriate for anomaly detection?

Authors:

Jonathan Timmis,

Claudia EckertAuthors Info & Claims

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

Pages 321 - 328

https://doi.org/10.1145/1068009.1068061

Published: 25 June 2005 Publication History

Abstract

Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algorithm in general, when applied to anomaly detection. A straightforward self detector classification principle is proposed and its classification performance is compared to a real-valued negative selection algorithm and to a one-class support vector machine. Earlier work suggests that real-value negative selection requires a single class to learn from. The investigations presented in this paper reveal, however, that when applied to anomaly detection, the real-valued negative selection and self detector classification techniques require positive and negative examples to achieve a high classification accuracy. Whereas, one-class SVMs only require examples from a single class.

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Cited By

Chen WYang YLiu L(2025)DE-PSA: Learning from unlabeled data by dual-stage label propagation for positive selection algorithmKnowledge-Based Systems10.1016/j.knosys.2024.112757309(112757)Online publication date: Jan-2025
https://doi.org/10.1016/j.knosys.2024.112757
Delo GMattone MSurace CWorden K(2024)Novelty detection across a small population of real structures: A negative selection approachJournal of Physics: Conference Series10.1088/1742-6596/2647/19/1920042647:19(192004)Online publication date: 1-Jun-2024
https://doi.org/10.1088/1742-6596/2647/19/192004
Liu XYang GWang LFu JWang Q(2024)A novel immune detector training method for network anomaly detectionApplied Intelligence10.1007/s10489-024-05288-254:2(2009-2030)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-024-05288-2
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Index Terms

Is negative selection appropriate for anomaly detection?
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Conferences

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

Copyright © 2005 ACM.

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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Publication History

Published: 25 June 2005

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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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Citations

Cited By

Chen WYang YLiu L(2025)DE-PSA: Learning from unlabeled data by dual-stage label propagation for positive selection algorithmKnowledge-Based Systems10.1016/j.knosys.2024.112757309(112757)Online publication date: Jan-2025
https://doi.org/10.1016/j.knosys.2024.112757
Delo GMattone MSurace CWorden K(2024)Novelty detection across a small population of real structures: A negative selection approachJournal of Physics: Conference Series10.1088/1742-6596/2647/19/1920042647:19(192004)Online publication date: 1-Jun-2024
https://doi.org/10.1088/1742-6596/2647/19/192004
Liu XYang GWang LFu JWang Q(2024)A novel immune detector training method for network anomaly detectionApplied Intelligence10.1007/s10489-024-05288-254:2(2009-2030)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-024-05288-2
Schröder GWortel ITextor J(2024)Population-Based Algorithms Built on Weighted AutomataParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70071-2_20(315-332)Online publication date: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70071-2_20
Béthune LNovello PCoiffier GBoissin TSerrurier MVincenot QTroya-Galvis AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Robust one-class classification with signed distance function using 1-Lipschitz neural networksProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618504(2245-2271)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3618504
Ruslan MSa'dan SBahrin UHamzah SYasin SIshak S(2023)Melanoma Recognition Using Negative Selection2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS)10.1109/AiDAS60501.2023.10284661(114-119)Online publication date: 6-Sep-2023
https://doi.org/10.1109/AiDAS60501.2023.10284661
Jayabharathi SIlango V(2023)Anomaly Detection Using Machine Learning Techniques: A Systematic ReviewAdvances in Data-Driven Computing and Intelligent Systems10.1007/978-981-99-3250-4_42(553-572)Online publication date: 4-Aug-2023
https://doi.org/10.1007/978-981-99-3250-4_42
Wen CChangzhi W(2022)Combine labeled and unlabeled data for immune detector training with label propagationKnowledge-Based Systems10.1016/j.knosys.2021.107661236:COnline publication date: 25-Jan-2022
https://dl.acm.org/doi/10.1016/j.knosys.2021.107661
Jagriti Lobiyal D(2022)An efficient self-organized traffic maintenance scheme employing positive selection algorithmMultimedia Tools and Applications10.1007/s11042-022-13174-781:23(33107-33125)Online publication date: 18-Apr-2022
https://doi.org/10.1007/s11042-022-13174-7
Chliah HBattou ABaz O(2022)Anomaly Detection-Based Negative Selection AlgorithmDistributed Sensing and Intelligent Systems10.1007/978-3-030-64258-7_9(99-105)Online publication date: 28-Jun-2022
https://doi.org/10.1007/978-3-030-64258-7_9
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