| Title | Network Security Situation Prediction in Software Defined Networking Data Plane |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Sheng, Mingren, Liu, Hongri, Yang, Xu, Wang, Wei, Huang, Junheng, Wang, Bailing |
| Conference Name | 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA) |
| Keywords | Artificial neural networks, bidirectional long short-term memory network, Communication networks, Computer science, cyber physical systems, Cyber-physical systems, Metrics, Network security, Neural Network Security, Neural networks, policy-based governance, Prediction algorithms, Predictive models, pubcrawl, Resiliency, security, software-defined network, Switches, time series classification |
| Abstract | Software-Defined Networking (SDN) simplifies network management by separating the control plane from the data forwarding plane. However, the plane separation technology introduces many new loopholes in the SDN data plane. In order to facilitate taking proactive measures to reduce the damage degree of network security events, this paper proposes a security situation prediction method based on particle swarm optimization algorithm and long-short-term memory neural network for network security events on the SDN data plane. According to the statistical information of the security incident, the analytic hierarchy process is used to calculate the SDN data plane security situation risk value. Then use the historical data of the security situation risk value to build an artificial neural network prediction model. Finally, a prediction model is used to predict the future security situation risk value. Experiments show that this method has good prediction accuracy and stability. |
| DOI | 10.1109/AEECA49918.2020.9213592 |
| Citation Key | sheng_network_2020 |
Network Security Situation Prediction in Software Defined Networking Data Plane