@Article{cmc.2020.06176,
AUTHOR = {Jieren Cheng, Junqi Li, Xiangyan Tang, Victor S. Sheng, Chen Zhang, Mengyang Li},
TITLE = {A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {3},
PAGES = {1423--1443},
URL = {http://www.techscience.com/cmc/v62n3/38364},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.06176}
}