REVIEW CLUSTERING MECHANISMS OF DISTRIBUTED DENIAL OF SERVICE ATTACKS
- 1 University of Babylon, Iraq
Copyright: © 2020 Wesam Bhaya and Mehdi Ebady Manaa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Distributed Denial of Service attacks (DDoS) overwhelm network resources with useless or harmful packets and prevent normal users from accessing these network resources. These attacks jeopardize the confidentiality, privacy and integrity of information on the internet. Since it is very difficult to set any predefined rules to correctly identify genuine network traffic, an anomaly-based Intrusion Detection System (IDS) for network security is commonly used to detect and prevent new DDoS attacks. Data mining methods can be used in intrusion detection systems, such as clustering k-means, artificial neural network. Since the clustering methods can be used to aggregate similar objects, they can detect DDoS attacks to reduce false-positive rates. In this study, a review of DDoS attacks using clustering data mining techniques is presented. A review illustrates the most recent, state-of-the art science for clustering techniques to detect DDoS attacks.
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- Network Security
- Distributed Denial of Service (DDoS)
- Data Mining