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Cluster Computing

, Volume 22, Supplement 1, pp 2505–2515 | Cite as

An efficient DDoS detection based on SU-Genetic feature selection

  • Chundong Wang
  • Honglei YaoEmail author
  • Zheli Liu
Article
  • 154 Downloads

Abstract

Distributed denial of service (DDoS) attack has been a huge challenge of network security for many years. The bandwidth, services and resources are seriously occupied by the attackers during the attack. It is vitally important to detect DDoS attacks effectively and efficiently. Aiming at the huge network traffic of DDoS attacks, the SU-Genetic method is proposed to select important features of the original attack data. The SU-Genetic method ranks features by the symmetrical uncertainty and then selects features with the genetic algorithm. The correlation evaluator with SU value is applied in genetic selection to balance the correlation and redundancy. After experimented on the NSL-KDD dataset, the features were reduced from 41 to 17 and the amount of data was roughly reduced to 41% of the original. Both the efficiency and accuracy of all the three classification-based detections (BayesNet, J48, and RanomTree) were improved with the proposed SU-Genetic feature selection method.

Keywords

DDoS detection Select features Symmetrical uncertainty Genetic search NSL-KDD 

Notes

Acknowledgements

Our work was supported by the Foundation of the Educational Commission of Tianjin, China (Grant No. 20130801), the General Project of Tianjin Municipal Science and Technology Commission under Grant (No. 15JCYBJC1 5600), the Major Project of Tianjin Municipal Science and Technology Commission under Grant (No. 15ZXDSGX00030), and NSFC: The United Foundation of General Technology and Fundamental Research (No. U1536122). The authors would like to give thanks to all the pioneers in this field, and also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the quality of this paper.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Computer Vision and System Ministry of EducationTianjin University of TechnologyTianjinChina
  2. 2.Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjinChina
  3. 3.School of Computer and Control EngineeringNankai UniversityTianjinChina

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