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Detecting Distributed Denial of Service Attacks Using Data Mining Techniques

Mouhammd Alkasassbeh, , Ahmad B.A Hassanat, Mohammad Almseidin
Published in The Science and Information (SAI) Organization
2016
Volume: 7
   
Issue: 1
Pages: 436 - 445
Abstract

Users and organizations find it continuously challenging to deal with distributed denial of service (DDoS) attacks. . The security engineer works to keep a service available at all times by dealing with intruder attacks. The intrusion-detection system (IDS) is one of the solutions to detecting and classifying any anomalous behavior. The IDS system should always be updated with the latest intruder attack deterrents to preserve the confidentiality, integrity and availability of the service. In this paper, a new dataset is collected because there were no common data sets that contain modern DDoS attacks in different network layers, such as (SIDDoS, HTTP Flood). This work incorporates three well-known classification techniques: Multilayer Perceptron (MLP), Naïve Bayes and Random Forest. The experimental results show that MLP achieved the highest accuracy rate (98.63%).

About the journal
JournalInternational Journal of Advanced Computer Science and Applications
PublisherThe Science and Information (SAI) Organization
ISSN2158107X
Open AccessNo