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Anomaly Detection Using Real-Valued Negative Selection

Authors:

Fabio A. González,

Dipankar DasguptaAuthors Info & Claims

Genetic Programming and Evolvable Machines, Volume 4, Issue 4

Pages 383 - 403

https://doi.org/10.1023/A:1026195112518

Published: 01 December 2003 Publication History

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Abstract

This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.

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Yang GWang LYu RHe JZeng BWu T(2023)A Modified Gray Wolf Optimizer-Based Negative Selection Algorithm for Network Anomaly DetectionInternational Journal of Intelligent Systems10.1155/2023/89808762023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/8980876
Kim YNam WLee J(2022)Multiclass anomaly detection for unsupervised and semi-supervised data based on a combination of negative selection and clonal selection algorithmsApplied Soft Computing10.1016/j.asoc.2022.108838122:COnline publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.108838
Wang DLiang YYang X(2022)NKA: a pathogen dose-based natural killer cell algorithm and its application to classificationThe Journal of Supercomputing10.1007/s11227-021-04133-478:5(7016-7037)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11227-021-04133-4
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Index Terms

Anomaly Detection Using Real-Valued Negative Selection
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
      2. Unsupervised learning
        Cluster analysis
    2. Machine learning approaches
      1. Classification and regression trees

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cover image Genetic Programming and Evolvable Machines

Genetic Programming and Evolvable Machines Volume 4, Issue 4

Dec 2003

95 pages

ISSN:1389-2576

Issue’s Table of Contents

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Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2003

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Yang GWang LYu RHe JZeng BWu T(2023)A Modified Gray Wolf Optimizer-Based Negative Selection Algorithm for Network Anomaly DetectionInternational Journal of Intelligent Systems10.1155/2023/89808762023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/8980876
Kim YNam WLee J(2022)Multiclass anomaly detection for unsupervised and semi-supervised data based on a combination of negative selection and clonal selection algorithmsApplied Soft Computing10.1016/j.asoc.2022.108838122:COnline publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.108838
Wang DLiang YYang X(2022)NKA: a pathogen dose-based natural killer cell algorithm and its application to classificationThe Journal of Supercomputing10.1007/s11227-021-04133-478:5(7016-7037)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11227-021-04133-4
Li ZLi T(2022)Using known nonself samples to improve negative selection algorithmApplied Intelligence10.1007/s10489-021-02323-452:1(482-500)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s10489-021-02323-4
Zhou WLiang Y(2022)An immune optimization based deterministic dendritic cell algorithmApplied Intelligence10.1007/s10489-020-02098-052:2(1461-1476)Online publication date: 1-Jan-2022
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Li DSun XGao FLiu S(2021)An improved real-valued negative selection algorithm based on the constant detector for anomaly detection Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-20040540:5(8793-8806)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JIFS-200405
Steinbuss GBöhm K(2021)Generating Artificial Outliers in the Absence of Genuine Ones — A SurveyACM Transactions on Knowledge Discovery from Data10.1145/344782215:2(1-37)Online publication date: 27-Mar-2021
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Chelly Dagdia ZAvdeyev PBayzid M(2021)Biological computation and computational biology: survey, challenges, and discussionArtificial Intelligence Review10.1007/s10462-020-09951-154:6(4169-4235)Online publication date: 27-Jan-2021
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Perkins PHeber SShi XBuck MMa JVeltri P(2019)Using a Novel Negative Selection Inspired Anomaly Detection Algorithm to Identify Corrupted Ribo-seq and RNA-seq SamplesProceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3307339.3342169(457-465)Online publication date: 4-Sep-2019
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Zhang RXiao X(2018)A Clone Selection Based Real-Valued Negative Selection AlgorithmComplexity10.1155/2018/25209402018Online publication date: 1-Jan-2018
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Is negative selection appropriate for anomaly detection?

Discriminating and visualizing anomalies using negative selection and self-organizing maps

Foundations of r-contiguous matching in negative selection for anomaly detection

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