<abstract xmlns="http://www.w3.org/1999/xhtml"><p>An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.</p></abstract>

An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.

Applying a Neural Network Ensemble to Intrusion Detection

Journal of Artificial Intelligence and Soft Computing Research

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

{"article-title":"Applying a Neural Network Ensemble to Intrusion Detection"}

An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within...

Applying a Neural Network Ensemble to Intrusion Detection

Online veröffentlicht: 09. Mai 2019

Seitenbereich: 177 - 188

Eingereicht: 27. Juli 2018

Akzeptiert: 19. Nov. 2018

DOI: https://doi.org/10.2478/jaiscr-2019-0002

Schlüsselwörter
Ensemble learning, Deep Neural Networks, NSL-KDD data set

© 2019 Simone A. Ludwig, published by Sciendo

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

Applying a Neural Network Ensemble to Intrusion Detection

Online veröffentlicht: 09. Mai 2019

Seitenbereich: 177 - 188

Eingereicht: 27. Juli 2018

Akzeptiert: 19. Nov. 2018

DOI: https://doi.org/10.2478/jaiscr-2019-0002

SchlüsselwörterEnsemble learning, Deep Neural Networks, NSL-KDD data set

© 2019 Simone A. Ludwig, published by Sciendo

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

Schlüsselwörter
Ensemble learning, Deep Neural Networks, NSL-KDD data set