| Title | Development and Optimization of Software Defined Networking Anomaly Detection Architecture by GRU-CNN under Deep Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Meng, Qinglan, Pang, Xiyu, Zheng, Yanli, Jiang, Gangwu, Tian, Xin |
| Conference Name | 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) |
| Keywords | anomaly detection, Communication networks, Computer architecture, convolutional neural network, Deep Learning, Gated Recurrent Unit, Network Security Architecture, Neural networks, pubcrawl, resilience, Resiliency, security, Signal processing algorithms, software defined networking, software reliability |
| Abstract | Ensuring the network security, resists the malicious traffic attacks as much as possible, and ensuring the network security, the Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) are combined. Then, a Software Defined Networking (SDN) anomaly detection architecture is built and continuously optimized to ensure network security as much as possible and enhance the reliability of the detection architecture. The results show that the proposed network architecture can greatly improve the accuracy of detection, and its performance will be different due to the different number of CNN layers. When the two-layer CNN structure is selected, its performance is the best among all algorithms. Especially, the accuracy of GRU- CNN-2 is 98.7%, which verifies that the proposed method is effective. Therefore, under deep learning, the utilization of GRU- CNN to explore and optimize the SDN anomaly detection is of great significance to ensure information transmission security in the future. |
| DOI | 10.1109/ICSP51882.2021.9408727 |
| Citation Key | meng_development_2021 |
Development and Optimization of Software Defined Networking Anomaly Detection Architecture by GRU-CNN under Deep Learning