Open AccessDistributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System
Author(s) -
Mukrimah Nawir,
Amiza Amir,
Naimah Yaakob,
Ahmad R. Badlishah,
Anuar Mat Safar,
Mohd Nazri Mohd Warip,
I. Zunaidi
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/557/1/012015
Subject(s) - computer science , anomaly detection , estimator , data mining , class (philosophy) , anomaly (physics) , algorithm , artificial intelligence , machine learning , mathematics , statistics , physics , condensed matter physics
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.