Knowledge graph reasoning for cyber attack detection

In today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human-like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in-depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security. In our interconnected world, cyber threats continuously evolve, presenting unprecedented challenges to cybersecurity. Conventional methods such as anomaly-based and feature-based approaches are encountering limitations and proving inadequate. The utilization of knowledge graph reasoning, leveraging graph structures, emerges as a promising paradigm shift in the landscape of cyberattack detection. This scholarly work delves into contemporary cybersecurity research, examining the potential of knowledge graph reasoning and proposing an innovative methodology with three principal objectives: optimizing data preparation for knowledge graph embedding models, establishing semantic foundations for network analysis via the system state graph ontology, and elevating network attack recognition through knowledge graph inference techniques. The study conducts experiments, comparing the proposed approach against existing methodologies, and demonstrates its efficacy in addressing the challenges associated with the escalating volume of network data. This approach signifies a promising trajectory towards automating network attack recognition and fortifying network security by seamlessly integrating knowledge graphs. image