Design and Analysis of Network Virus Defense System Based on Multimodal Data Mining Technology

Abstract

This study analyzes and designs a network security management system based on the K-means algorithm. Through the investigation of network security managers, this article uses structured modeling technology to derive the system’s logical business functions, which are data acquisition function, data analysis function, and post-processing function. This study uses a three-tier architecture to design the system, describes the system data business processing flow, and gives the key flow of the K-means algorithm application. In addition, the system database is designed to provide support for system data processing. This study proposes a dual-network text matching model based on local interactive components. This model composes two modalities of single-modal short text data: a positional structural modal for describing local interactions and a global semantic understanding modal for extracting global semantic information. Two heterogeneous networks are used to extract two modalities. The principle of complementarity of modalities is realized by constructing the differences of each modal. At the same time, through the attention mechanism, the position information of the position structure modal is transferred to the global semantic understanding modal to obtain consistent comprehensive information so as to realize the principle of modal consistency. On this basis, by designing low-order interaction functions and high-order interaction functions, and using long- and short-term memory networks, we have, respectively, improved the position structure modal and global semantic understanding modal. The theoretical analysis and simulation results of the model show that the propagation characteristics of the network worm are completely determined by the threshold R0 required for its existence. When R0 >1, the network worm will become popular even with a small initial infection number; conversely, even if the initial infection machine is large, the network worm will eventually become extinct. The research results of this article also show that the introduction of mobile devices has an important impact on the defense technology of network worms. To curb network worms that use both the network and mobile devices to spread, and to increase the proportion of machines that have never been infected with the virus, the most effective method is to control the number of mobile devices and reduce the possibility of cross-infection between mobile devices and machines. The simulation results show that, regardless of whether dynamic isolation and antivirus software are considered, the local area network-based choke method given in this study can effectively curb intelligent network worms that have slow scanning rates and clear scanning targets. In addition, the choke method presented in this article can determine the choke threshold without affecting the normal network scanning behavior of users in the local area network.