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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: Nawir, M., Amir, A., Lynn, O.B., Yaakob, N., Ahmad, R.B.
Format: Conference or Workshop Item
Language:English
English
Published: 2018
Subjects:
QA75 Electronic computers. Computer science
T Technology (General)
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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spelling my-unisza-ir.16882020-11-19T07:35:33Z http://eprints.unisza.edu.my/1688/ Performances of machine learning algorithms for binary classification of network anomaly detection system Nawir, M. Amir, A. Lynn, O.B. Yaakob, N. Ahmad, R.B. QA75 Electronic computers. Computer science T Technology (General) 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. 2018 Conference or Workshop Item NonPeerReviewed image en http://eprints.unisza.edu.my/1688/1/FH03-FIK-18-14168.jpg text en http://eprints.unisza.edu.my/1688/2/FH03-FIK-18-16951.pdf Nawir, M. and Amir, A. and Lynn, O.B. and Yaakob, N. and Ahmad, R.B. (2018) Performances of machine learning algorithms for binary classification of network anomaly detection system. In: 1st International Conference on Big Data and Cloud Computing, 25-27 Nov 2017, Kuching, Sarawak.
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
T Technology (General)
Nawir, M.
Amir, A.
Lynn, O.B.
Yaakob, N.
Ahmad, R.B.
Performances of machine learning algorithms for binary classification of network anomaly detection system
description 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.
format Conference or Workshop Item
author Nawir, M.
Amir, A.
Lynn, O.B.
Yaakob, N.
Ahmad, R.B.
author_facet Nawir, M.
Amir, A.
Lynn, O.B.
Yaakob, N.
Ahmad, R.B.
author_sort Nawir, M.
title Performances of machine learning algorithms for binary classification of network anomaly detection system
title_short Performances of machine learning algorithms for binary classification of network anomaly detection system
title_full Performances of machine learning algorithms for binary classification of network anomaly detection system
title_fullStr Performances of machine learning algorithms for binary classification of network anomaly detection system
title_full_unstemmed Performances of machine learning algorithms for binary classification of network anomaly detection system
title_sort performances of machine learning algorithms for binary classification of network anomaly detection system
publishDate 2018
url 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/
_version_ 1684657737568354304
score 13.211869

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