Year: 2025
Type: article
Abstract: With the rapid development of computer network technology, virus attack methods are becoming increasingly complex and concealed, and traditional defense methods relying on signature database matching have difficulty coping with unknown threats. Data mining technology, with its powerful data processing and pattern discovery capabilities, provides new ideas for network virus defense. By analyzing massive amounts of data such as network traffic and system logs, the potential patterns of virus behavior can be mined, and dynamic detection models can be constructed to achieve a transformation from passive response to active defense. The research combines classification, clustering, and association rule algorithms to explore key technologies for virus feature extraction and behavior prediction, aiming to break through the limitations of traditional methods, improve detection efficiency and accuracy, and lay a foundation for building an intelligent and adaptive network security system.
Author: Wang Zeding
Institution: North China Electric Power University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411668330