A multi-filter feature selection in detecting distributed denial-of-service attack

Author:

Jun Yon Yi1,Leau Yu-Beng1,Alias Suraya1,Park Yong Jin1

Affiliation:

1. Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia

Funder

Universiti Malaysia Sabah

Publisher

ACM

Reference19 articles.

1. Bhatia S. 2013. Detecting distributed denial-of-service attacks and flash events. Queensland University of Technology. Bhatia S. 2013. Detecting distributed denial-of-service attacks and flash events. Queensland University of Technology.

2. Wang C. Yao H. & Liu Z. (2018). An efficient DDoS detection based on SU-Genetic feature selection. Cluster Computing. DOI=http:// 10.1007/s10586-018-2275-z Wang C. Yao H. & Liu Z. (2018). An efficient DDoS detection based on SU-Genetic feature selection. Cluster Computing. DOI=http:// 10.1007/s10586-018-2275-z

3. Discriminating ddos attack traffic from flash crowds on internet threat monitors (itm) using entropy variations;Prasad K. M.;African Journal of Computing & ICT,2013

4. Yu L. & Liu H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. Paper presented at the Proceedings of the 20th international conference on machine learning (ICML-03). Yu L. & Liu H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. Paper presented at the Proceedings of the 20th international conference on machine learning (ICML-03).

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