Design of network information visualization security cognition system based on QSOFM network and FR algorithm

Abstract

ABSTRACT Network information is complex, there is no lack of hidden risks of information to the user caused by bad effects. But analyzing and evaluating them is complex and requires more diverse and powerful techniques. This research will combine Fruchterman-Reingold (FR) information visualization algorithm and information security risk assessment method based on Quantum self-organizing feature map (QSOFM) network to build information visualization security cognitive system. By introducing quantum neurons into self-organizing feature mapping network (SOFM), a network information security evaluation model is constructed. The system can evaluate the security level of network information and visualize the evaluation results. The experimental results show that the system has fast response speed and strong interactivity. The accuracy of QSOFM algorithm is 86%, 12% higher than the traditional algorithm, which can provide more powerful technical support for the visual security cognition of network information.