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A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

Jieren Cheng1, 2, Junqi Li2, *, Xiangyan Tang2, Victor S. Sheng3, Chen Zhang2, Mengyang Li2

1 Key Laboratory of Internet Information Retrieval of Hainan Province, Hainan University, Haikou, China.
2 College of Information Science & Technology, Hainan University, Haikou, China.
3 Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA.

* Corresponding Author: Junqi Li. Email: .

Computers, Materials & Continua 2020, 62(3), 1423-1443.


Distributed Denial of Service (DDoS) attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security. Existing detection methods cannot effectively detect early attacks. In this paper, we propose a detection method of DDoS attacks based on generalized multiple kernel learning (GMKL) combining with the constructed parameter R. The super-fusion feature value (SFV) and comprehensive degree of feature (CDF) are defined to describe the characteristic of attack flow and normal flow. A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm. A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter. The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection, and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate.


Cite This Article

J. Cheng, J. Li, X. Tang, V. S. Sheng, C. Zhang et al., "A novel ddos attack detection method using optimized generalized multiple kernel learning," Computers, Materials & Continua, vol. 62, no.3, pp. 1423–1443, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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