Simulation of Network Security Situation Awareness based on Data Analysis

The internet has become an indispensable infrastructure in modern society, but cybersecurity issues are becoming increasingly prominent. In the current context of the increasing popularity of complex heterogeneous networks, the research results of the security situational awareness problem for complex networks such as the Internet of Things (IoT) and the Internet will be directly related to the normal operation status of these networks, as well as the data information security and privacy protection of the state and individuals. In this paper, the deep neural network algorithm is used to train and model the data of network posture to achieve accurate identification of network attack events. Experimental data shows that the accuracy of the system on the training set, validation set, and test set is 98.5%, 97.8%, and 98.2%, respectively, demonstrating excellent detection and response capabilities. By optimizing the convolutional neural network structure, the system has improved in terms of accuracy, false positive rate, and computational cost. The research results indicate that the platform can effectively enhance the perception ability of network security situation, providing solid technical support for future network security protection.