Publication:
Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

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cris.author.scopus-author-id 57193134132
cris.author.scopus-author-id 36170326400
cris.author.scopus-author-id 36728195700
cris.author.scopus-author-id 24826032200
cris.author.scopus-author-id 57194844651
dc.contributor.author Nawir M.
dc.contributor.author Amir A.
dc.contributor.author Lynn O.B.
dc.contributor.author Yaakob N.
dc.contributor.author Badlishah Ahmad R.
dc.date.accessioned 2025-01-13T14:41:30Z
dc.date.available 2025-01-13T14:41:30Z
dc.date.issued 2018-06-01
dc.description.abstract 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.
dc.identifier.doi 10.1088/1742-6596/1018/1/012015
dc.identifier.scopus 2-s2.0-85048364778
dc.identifier.uri https://hdl.handle.net/20.500.14170/13383
dc.relation.grantno undefined
dc.relation.ispartof Journal of Physics: Conference Series
dc.relation.ispartofseries Journal of Physics: Conference Series
dc.relation.issn 17426588
dc.rights open access
dc.title Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.issue 1
oaire.citation.volume 1018
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Sultan Zainal Abidin
oairecerif.citation.number 012015
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person.identifier.scopus-author-id 57193134132
person.identifier.scopus-author-id 36170326400
person.identifier.scopus-author-id 36728195700
person.identifier.scopus-author-id 24826032200
person.identifier.scopus-author-id 57194844651
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