Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

International Journal of artificial intelligence research

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Field Value
 
Title Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection
 
Creator Muhammad, Arif Wirawan
Foozy, Cik Feresa Mohd
Azhari, Ahmad
 
Subject
IDS;DDoS;Feature;Machine Learning;
 
Description Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity.  DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the  deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development.  According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%.
 
Publisher STMIK Dharma Wacana
 
Contributor
 
Date 2020-02-19
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijair.id/index.php/ijair/article/view/156
10.29099/ijair.v4i1.156
 
Source International Journal of Artificial Intelligence Research; Vol 4, No 1 (2020): June; 1-8
2579-7298
10.29099/ijair.v4i1
 
Language eng
 
Relation http://ijair.id/index.php/ijair/article/view/156/pdf
 
Rights Copyright (c) 2020 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0
 

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