基于Renyi熵和BiGRU算法实现SDN环境下的DDoS攻击检测方法

DDoS Attack Detection Method in SDN Environment Based on Renyi Entropy and BiGRU Algorithm

基于双向的门控循环单元(Bidirectional Gated Recurrent Unit,BiGRU)网络能够解决传统RNN模型存在的梯度消失或梯度爆炸问题,文中提出了一种基于Renyi熵和BiGRU算法实现SDN(Software Defined Network)环境下的DDoS攻击检测方法,首先应用Renyi熵进行异常流量检测,检测划分为正常、异常两种结果,检测为异常的流量将应用BiGRU(bi-gatedrecurrentunit,BiGRU)算法进行攻击检测;然后利用交换机收集流表信息,提取了6个特征向量作为攻击检测的特征向量,最后通过Mininet模拟SDN的网络拓扑结构,基于控制器OpenDaylight完成检测。实验结果表明:相比SVM和BPNN神经网络检测算法,所提检测方案的检测准确率和识别率更高,有较好的综合检测能力。

Based on the bidirectional gated recurrent unit,BiGRU network can solve the gradient disappearance or gradient explosion problem of the traditional RNN model,a DDoS attack detection method in SDN environment based on Renyi entropy and bigru algorithm is proposed.First of all,the abnormal flow detection is carried out by Renyi entropy,and the detection is divided into normal and abnormal results.Traffic detected as abnormal will be detected using the BiGRU algorithm.Then,the switch is used to collect flow meter information,6 feature vectors are extracted as the characteristic vectors of attack detection.Finally,the network topology of the SDN is simulated by Minet,which is based on the controller OpenDaylight.The experimental results show that compared with SVM and BPNN neural network detection algorithm,the proposed detection scheme has improved detection accuracy,higher recognition rate and better comprehensive detection capability.