Research on the Construction of Network Security Attack Detection Model Based on Knowledge Graph

With the rapid development of internet technology and its extensive and deep application, network security issues have increasingly become a critical factor constraining the development of the digital economy and information society. The means of network attacks are constantly being innovated, ranging from traditional viruses and Trojans to recent ransomware and zero-day attacks. The complexity and stealth of these methods pose significant challenges to network security defenses. Therefore, how to effectively detect and defend against these network attacks has become an important topic in both research and practice. Among the many network security defense technologies, Intrusion Detection Systems (IDS) serve as a crucial means of defense by monitoring network traffic and system activities to identify potential malicious behaviors. However, with the advancement of attack techniques, traditional detection methods based on characteristics and behaviors face severe challenges, especially in terms of their effectiveness in detecting new and complex attacks. In recent years, knowledge graphs, as an effective tool for knowledge management and reasoning, have shown tremendous potential in multiple domains, including the semantic web, recommendation systems, natural language processing, and more. They can integrate dispersed, heterogeneous data into a structured knowledge base and provide rich contextual information through semantic associations, enabling deeper understanding and analysis of data. Therefore, applying knowledge graph technology to network security attack detection can enhance the identification of known attack patterns and discover new attack paths and potential threats through knowledge reasoning. This study aims to explore the construction methods of a network security attack detection model based on knowledge graphs. By thoroughly analyzing the characteristics and patterns of network attack behaviors and building a network security knowledge graph, combined with advanced machine learning and data mining technologies, we have designed and implemented a novel network attack detection model. This model not only effectively improves the detection accuracy of known attack types but also can discover new attack patterns through knowledge reasoning, providing more robust and intelligent support for network security defense.