Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.

The wide application of the Internet and the use of various network devices make the existing network scale increasingly complex, and the hidden dangers of various network security incidents are gradually exposed. Software Defined Network (SDN) brings potential security risks and challenges while changing the underlying network structure. In this paper, a situation assessment model based on SDN network is proposed by summarizing the important indicators and parameters of SDN. Then, the situational factors are mapped to the layers of the neural network, and the weights and thresholds of the back propagation(BP) neural network are optimized by the improved algorithm to obtain the global optimal solution. Finally, the purpose of situation assessment and comprehensive rating of the SDN network is realized. The experimental results show that the situation assessment method proposed in this paper has good effect and high accuracy, which provides a better basis for the next situation prediction.