A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning |
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Authors: | Jieren Cheng Junqi Li Xiangyan Tang Victor S Sheng Chen Zhang Mengyang Li |
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Affiliation: | 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. |
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Abstract: | 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. |
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Keywords: | DDoS attack detection GMKL parameter optimization |
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