<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoen-coder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together.</p>
<p>We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.</p>
</abstract>

Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoen-coder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together.
We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.

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

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can...

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

Publié en ligne: 23 févr. 2022

Pages: 149 - 163

Reçu: 15 déc. 2021

Accepté: 30 janv. 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0010

Mots clés
intrusion detection, neural network, ensemble classifiers, hyperparameter optimization, sparse autoencoder, NSL-KDD, machine learning

© 2022 Csaba Brunner et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Publié en ligne: 23 févr. 2022

Pages: 149 - 163

Reçu: 15 déc. 2021

Accepté: 30 janv. 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0010

Mots clésintrusion detection, neural network, ensemble classifiers, hyperparameter optimization, sparse autoencoder, NSL-KDD, machine learning

© 2022 Csaba Brunner et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
intrusion detection, neural network, ensemble classifiers, hyperparameter optimization, sparse autoencoder, NSL-KDD, machine learning