文章基本信息

标题：Detecting Distributed Denial of Service Attacks Using Data Mining Techniques
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作者：Mouhammd Alkasassbeh ; Ghazi Al-Naymat ; Ahmad B.A Hassanat 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：1
DOI：10.14569/IJACSA.2016.070159
出版社：Science and Information Society (SAI)
摘要：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%).
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; DDoS; IDS; MLP; Naïve Bayes; Random Forest