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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

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.

Volume 12 (2022): Issue 2 (April 2022)
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