Research on intelligent penetration mechanism of netgap data based on database adaptive matching

Abstract

The purpose of data penetration is to achieve timely exchange of data information while ensuring the security of network isolation environment. The current research mainly focuses on protecting data privacy and developing basic data penetration methods for netgap, but these methods generally overlook the adaptive matching of data. Therefore, this article proposes an intelligent netgap data penetration mechanism based on database adaptive matching, which is an end-to-end cross netgap data packet adaptation mechanism. To overcome the problems of overfitting and local optima, XGBoost based on adaptive particle swarm optimization is adopted. By increasing the diversity of subgroups, the adaptive matching effect was improved, and the optimized XGBoost model was fitted to real-time data, with an accuracy increase from 72% to 94%. The experimental results show that this mechanism significantly improves the efficiency of packet adaptive matching while solving the problem of data penetration in the isolated environment of the network gate. The time cost is only 50% of the previous one, which improves the throughput of the system. The optimized XGBoost model is superior to the original model in terms of error and accuracy, and has certain practicality and easy generalization.