Performances of machine learning algorithms for binary classification of network anomaly detection system
The rapid growth of technologies might endanger them to various network attacks due to the nature of data which are frequently exchange their data through Internet and large-scale data that need to be handle. Moreover, network anomaly detection using machine learning faced difficulty when dealing...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/1688/1/FH03-FIK-18-14168.jpg http://eprints.unisza.edu.my/1688/2/FH03-FIK-18-16951.pdf http://eprints.unisza.edu.my/1688/ |
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| Summary: | The rapid growth of technologies might endanger them to various network attacks due to the nature
of data which are frequently exchange their data through Internet and large-scale data that need to
be handle. Moreover, network anomaly detection using machine learning faced difficulty when
dealing the involvement of dataset where the number of labelled network dataset is very few in
public and this caused many researchers keep used the most commonly network dataset (KDDCup99)
which is not relevant to employ the machine learning (ML) algorithms for a classification. Several
issues regarding these available labelled network datasets are discussed in this paper. The aim of this
paper to build a network anomaly detection system using machine learning algorithms that are
efficient, effective and fast processing. The finding showed that AODE algorithm is performed well in
term of accuracy and processing time for binary classification towards UNSW-NB15 dataset. |
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