Network Security Posture Assessment Algorithm Based on Multilayer Perceptron of Graph Convolutional Neural Networks

With the increasing complexity and security threats in cyberspace, network security situation assessment has become a key technology to ensure digital security. This study proposes a hybrid model integrating graph convolutional neural networks and multi-layer perceptrons to address the limitations of traditional methods in capturing the topological associations of complex networks and dynamic threat responses. First, this model uses graph convolutional neural networks to aggregate node neighborhood information and capture topological features. Then, it conducts deep nonlinear feature learning through multi-layer perceptrons. Finally, it screens key information through pooling layers. Finally, the situation level assessment is achieved by the Softmax classifier. Experiments showed that the accuracy rates of the model on the CICIDS and UNSW-NB15 datasets reached 96.5% and 94.3% respectively, and its performance was superior to that of the comparison models. In the simulation and dynamic environment tests, the model evaluation results were stable, with an average evaluation time of only 66.19 ms and a resource utilization rate of 53.87%. The hybrid model constructed in this study effectively overcomes the challenges of feature fusion and classification in complex network environments. It provides a novel solution for efficiently and accurately assessing network security situations and has significant practical application value.