A Cooperative Detection of DDoS Attacks Based on CNN-BiLSTM in SDN

Hongwei Zhou
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Abstract

In view of the problem that detecting DDoS attack traffic in traditional SDN depends on the controller continuously collecting traffic and running the detection model, resulting in excessive controller overhead, low detection efficiency, increased traffic forwarding delay, and easy to cause "single point of failure", a cooperative detection method of DDoS attack in SDN based on information entropy and deep learning is proposed, which divides part of the detection task into the data plane for detection based on information entropy and uses the improved CNN-BiLSTM model to detect DDoS attack traffic on control plane. The experimental results show that, compared with the SVC-RF method in recent years, the accuracy of the proposed CNN-BiLSTM model is increased by 0.74%, the detection rate is increased by 1.42%, and the false alarm rate is reduced by 1.5%. Compared with the BiLSTM model, the accuracy is increased by 0.75%, the detection rate is increased by 0.64%, and the false alarm rate is reduced by 1.14%. Compared with the RF method, the accuracy is increased by 2.34%, the detection rate is increased by 3.88%, and the false alarm rate is reduced by 4%. Compared with the traditional single point detection method which only depends on the controller, the proposed switch-controller cooperative detection method reduces the CPU occupancy of the controller by about 12% and the detection time by about 13 seconds.
SDN中基于CNN-BiLSTM的DDoS攻击协同检测
针对传统SDN中DDoS攻击流量检测依赖于控制器不断采集流量并运行检测模型,导致控制器开销过大、检测效率低、流量转发延迟增大、易造成“单点故障”的问题,提出了一种基于信息熵和深度学习的SDN中DDoS攻击协同检测方法。基于信息熵将部分检测任务划分到数据平面进行检测,利用改进的CNN-BiLSTM模型对控制平面的DDoS攻击流量进行检测。实验结果表明,与近年来的SVC-RF方法相比,本文提出的CNN-BiLSTM模型的准确率提高了0.74%,检测率提高了1.42%,虚警率降低了1.5%。与BiLSTM模型相比,准确率提高了0.75%,检测率提高了0.64%,虚警率降低了1.14%。与RF方法相比,准确率提高2.34%,检出率提高3.88%,虚警率降低4%。与仅依赖控制器的传统单点检测方法相比,本文提出的开关控制器协同检测方法使控制器的CPU占用率降低了12%左右,检测时间缩短了13秒左右。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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