The Use of Multi-Objective Genetic Algorithm Based Approach to Create Ensemble of ANN for Intrusion Detection

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International Journal of Intelligence Science

ISSN Print: 2163-0283
ISSN Online: 2163-0356
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"The Use of Multi-Objective Genetic Algorithm Based Approach to Create Ensemble of ANN for Intrusion Detection"
written by Gulshan Kumar, Krishan Kumar,
published by International Journal of Intelligence Science, Vol.2 No.4A, 2012

has been cited by the following article(s):

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[8]	Artificial Intelligence based Ensemble Approach for Intrusion Detection Systems
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[9]	An improved ensemble approach for effective intrusion detection
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[10]	Machine Learning-Based Prediction System For Chronic Kidney Disease Using Associative Classification Technique
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[11]	Intrusion detection System based on Hybrid Whale-Genetic Algorithm
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[13]	Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network
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[14]	An intrusion detection system based on a hybrid Tabu-genetic algorithm
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[16]	A Multi-objective Genetic Algorithm Based Approach for Effective Intrusion Detection Using Neural Networks
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[17]	A survey of multi-objective evolutionary algorithms for data mining: Part-II
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[18]	The Multi-Objective Genetic Algorithm Based Techniques for Intrusion Detection
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[19]	Survey of multiobjective evolutionary algorithms for data mining: part II
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[20]	The genetic algorithm optimize computing applications in computer network reliability analysis
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[21]	Design of an evolutionary approach for intrusion detection
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[1]	Parallel Model to Detect Attacks Using Evolutionary Based Technique 2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), 2023 DOI:10.1109/ACCESS57397.2023.10200912

[2]	Evolutionary Computing and Mobile Sustainable Networks Lecture Notes on Data Engineering and Communications Technologies, 2022 DOI:10.1007/978-981-16-9605-3_10

[3]	A fuzzy computational approach for selecting interdependent projects using prioritized criteria Journal of Intelligent & Fuzzy Systems, 2021 DOI:10.3233/JIFS-202506

[4]	MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review The Journal of Supercomputing, 2020 DOI:10.1007/s11227-020-03196-z

[5]	Business and Consumer Analytics: New Ideas 2019 DOI:10.1007/978-3-030-06222-4_20

[6]	Grouping detection and forecasting security controls using unrestricted cooperative bargains Computer Communications, 2019 DOI:10.1016/j.comcom.2019.07.022

[7]	An improved ensemble approach for effective intrusion detection The Journal of Supercomputing, 2019 DOI:10.1007/s11227-019-03035-w

[8]	Artificial Intelligence based Ensemble Approach for Intrusion Detection Systems Journal of Visual Communication and Image Representation, 2019 DOI:10.1016/j.jvcir.2019.102736

[9]	Intrusion detection System based on Hybrid Whale-Genetic Algorithm 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), 2018 DOI:10.1109/ICICCT.2018.8473082

[10]	Intrusion Detection Using Deep Belief Network and Probabilistic Neural Network 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 2017 DOI:10.1109/CSE-EUC.2017.119

[11]	Intelligent Methods for Cyber Warfare Studies in Computational Intelligence, 2015 DOI:10.1007/978-3-319-08624-8_8

[12]	Intelligent Methods for Cyber Warfare Studies in Computational Intelligence, 2015 DOI:10.1007/978-3-319-08624-8_8

[13]	Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II IEEE Transactions on Evolutionary Computation, 2014 DOI:10.1109/TEVC.2013.2290082

[14]	Design of an Evolutionary Approach for Intrusion Detection The Scientific World Journal, 2013 DOI:10.1155/2013/962185

[15]	Design of an Evolutionary Approach for Intrusion Detection The Scientific World Journal, 2013 DOI:10.1155/2013/962185

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