1. bookVolume 9 (2019): Issue 3 (July 2019)
Journal Details
License
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

Applying a Neural Network Ensemble to Intrusion Detection

Published Online: 09 May 2019
Volume & Issue: Volume 9 (2019) - Issue 3 (July 2019)
Page range: 177 - 188
Received: 27 Jul 2018
Accepted: 19 Nov 2018
Journal Details
License
Format
Journal
eISSN
2449-6499
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
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.

Keywords

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