Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System

Badlishah, Ahmad and Nawir, M. and Amir, A and Yaakob, N and Mat Safar, A and Mohd Warip, M.N and Zunaidi, I (2019) Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System. In: 1st International Conference on Mechanical Electronic and Biosystem Engineering, 15-16 December 2018, Bogor; Indonesia.

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Abstract

Network monitoring system consists of large data streams, distributed architecture, and multiple computers that are geographically located all over the world caused a difficulty to detect abnormalities in the system. In addition, when handling network traffic, the data in network is fast incoming and requires an online learning where immediately response and predict the pattern of network traffic for classification once there is an event or request occur. Therefore, this paper aims to develop an effective and efficient network anomaly detection system by using distributed online averaged one dependence estimator (DOAODE) classification algorithm for multi-class network data to overcome these issues. The finding of DOAODE algorithm for multi-class classification is high in accuracy with average 83% and fast to train the network traffic recorded less than ten seconds and takes shorter time when the number of nodes increases. © Published under licence by IOP Publishing Ltd.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Averaged one-dependence estimators, Classification algorithm, Difficulty to detect, Distributed architecture, Multi-class classification, Multiclass networks, Network anomaly detection, Network monitoring systems
Subjects: T Technology > T Technology (General)
Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: Faculty of Informatics & Computing
Depositing User: Muhammad Akmal Azhar
Date Deposited: 01 Feb 2021 01:26
Last Modified: 01 Feb 2021 01:26
URI: http://eprints.unisza.edu.my/id/eprint/2559

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