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 th...

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Bibliographic Details
Published in:Journal of physics. Conference series 2018-05, Vol.1018 (1), p.12015
Main Authors: Nawir, Mukrimah, Amir, Amiza, Lynn, Ong Bi, Yaakob, Naimah, Badlishah Ahmad, R.
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Classification
Datasets
Machine learning
Physics
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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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1018/1/012015