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.
Index Terms—Anomaly detection, distributed denial of
service attacks, deep learning, software defined network
Hongwei Zhou is with School of computer Science of Guangdong
University of Technology, Guangzhou, China. E-mail: 434895488@qq.com
(H.W.Z.)
Cite: Hongwei Zhou, "A Cooperative Detection of DDoS Attacks Based on CNN-BiLSTM in SDN," International Journal of Future Computer and Communication vol. 12, no. 2, pp. 27-36, 2023.
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