  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 can not 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.
