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An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection Cover

An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection

Journal of Artificial Intelligence and Soft Computing Research
Volume 12 (2022): Issue 2 (April 2022)
By: Csaba Brunner,  Andrea Kő and  Szabina Fodor  
Open Access
|Feb 2022

Authors

Csaba Brunner

Department of Information Systems, Corvinus University of Budapest, Budapest, Hungary

Andrea Kő

Department of Information Systems, Corvinus University of Budapest, Budapest, Hungary

Szabina Fodor

szabina.fodor@uni-corvinus.hu

Department of Computer Science, Corvinus University of Budapest, Budapest, Hungary
DOI: https://doi.org/10.2478/jaiscr-2022-0010
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Language: English
Page range: 149 - 163
Submitted on: Dec 15, 2021
|
Accepted on: Jan 30, 2022
|
Published on: Feb 23, 2022
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
intrusion detection,
neural network,
ensemble classifiers,
hyperparameter optimization,
sparse autoencoder,
NSL-KDD,
machine learning
Related subjects:
Computer sciences,
Databases and data mining,
Artificial intelligence

© 2022 Csaba Brunner, Andrea Kő, Szabina Fodor, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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