article

Genetic algorithm with variable length chromosomes for network intrusion detection

Authors:

Sunil Nilkanth Pawar,

Rajankumar Sadashivrao BichkarAuthors Info & Claims

International Journal of Automation and Computing, Volume 12, Issue 3

Pages 337 - 342

https://doi.org/10.1007/s11633-014-0870-x

Published: 01 June 2015 Publication History

Abstract

Genetic algorithm (GA) has received significant attention for the design and implementation of intrusion detection systems. In this paper, it is proposed to use variable length chromosomes (VLCs) in a GA-based network intrusion detection system. Fewer chromosomes with relevant features are used for rule generation. An effective fitness function is used to define the fitness of each rule. Each chromosome will have one or more rules in it. As each chromosome is a complete solution to the problem, fewer chromosomes are sufficient for effective intrusion detection. This reduces the computational time. The proposed approach is tested using Defense Advanced Research Project Agency (DARPA) 1998 data. The experimental results show that the proposed approach is efficient in network intrusion detection.

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Piotrowski APiotrowska A(2022)Differential evolution and particle swarm optimization against COVID-19Artificial Intelligence Review10.1007/s10462-021-10052-w55:3(2149-2219)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10462-021-10052-w
Ayyagari MKesswani NKumar MKumar K(2021)Intrusion detection techniques in network environment: a systematic reviewWireless Networks10.1007/s11276-020-02529-327:2(1269-1285)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11276-020-02529-3
Ghannami ALi JHawbani AAlhusaini N(2021)Diversity metrics for direct-coded variable-length chromosome shortest path problem evolutionary algorithmsComputing10.1007/s00607-020-00851-4103:2(313-332)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s00607-020-00851-4
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Index Terms

Genetic algorithm with variable length chromosomes for network intrusion detection
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image International Journal of Automation and Computing

International Journal of Automation and Computing Volume 12, Issue 3

June 2015

114 pages

ISSN:1476-8186

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Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2015

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Piotrowski APiotrowska A(2022)Differential evolution and particle swarm optimization against COVID-19Artificial Intelligence Review10.1007/s10462-021-10052-w55:3(2149-2219)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10462-021-10052-w
Ayyagari MKesswani NKumar MKumar K(2021)Intrusion detection techniques in network environment: a systematic reviewWireless Networks10.1007/s11276-020-02529-327:2(1269-1285)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11276-020-02529-3
Ghannami ALi JHawbani AAlhusaini N(2021)Diversity metrics for direct-coded variable-length chromosome shortest path problem evolutionary algorithmsComputing10.1007/s00607-020-00851-4103:2(313-332)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s00607-020-00851-4
Hajj SEl Sibai RBou Abdo JDemerjian JMakhoul AGuyeux C(2021)Anomaly‐based intrusion detection systemsTransactions on Emerging Telecommunications Technologies10.1002/ett.424032:4Online publication date: 5-Apr-2021
https://dl.acm.org/doi/10.1002/ett.4240
Viswambaran RChen GXue BNekooei M(2020)Evolving Deep Recurrent Neural Networks Using A New Variable-Length Genetic Algorithm2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185851(1-8)Online publication date: 19-Jul-2020
https://dl.acm.org/doi/10.1109/CEC48606.2020.9185851
Zhang JCai JMeng YMeng T(2020)A novel self-adaptive Circuit design technique based on evolvable hardwareInternational Journal of Automation and Computing10.1007/s11633-016-1000-817:5(744-751)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s11633-016-1000-8
Akinrolabu OAgrafiotis IErola A(2018)The challenge of detecting sophisticated attacksProceedings of the 13th International Conference on Availability, Reliability and Security10.1145/3230833.3233280(1-9)Online publication date: 27-Aug-2018
https://dl.acm.org/doi/10.1145/3230833.3233280
Li DLi YLi JFu Y(2018)Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic AlgorithmInternational Journal of Automation and Computing10.1007/s11633-017-1107-615:3(267-276)Online publication date: 1-Jun-2018
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Keegan NJi SChaudhary AConcolato CYu BJeong D(2016)A survey of cloud-based network intrusion detection analysisHuman-centric Computing and Information Sciences10.1186/s13673-016-0076-z6:1(1-16)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1186/s13673-016-0076-z

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A fuzzy-genetic approach to network intrusion detection

Syntax vs. semantics: competing approaches to dynamic network intrusion detection

A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems

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