Research Article Open Access

REVIEW CLUSTERING MECHANISMS OF DISTRIBUTED DENIAL OF SERVICE ATTACKS

Wesam Bhaya1 and Mehdi Ebady Manaa1
  • 1 University of Babylon, Iraq
Journal of Computer Science
Volume 10 No. 10, 2014, 2037-2046

DOI: https://doi.org/10.3844/jcssp.2014.2037.2046

Submitted On: 15 March 2014 Published On: 26 June 2014

How to Cite: Bhaya, W. & Manaa, M. E. (2014). REVIEW CLUSTERING MECHANISMS OF DISTRIBUTED DENIAL OF SERVICE ATTACKS. Journal of Computer Science, 10(10), 2037-2046. https://doi.org/10.3844/jcssp.2014.2037.2046

Abstract

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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Keywords

  • Network Security
  • Distributed Denial of Service (DDoS)
  • Data Mining