Beyond Traditional Methods: A NLP and Knowledge Graph Approach to Cyber Threat Detection and Visualization

Le résumé fourni par la source

Cyber threat detection, analysis, and mitigation are becoming more and more complex, as traditional signature-based cybersecurity methods tend to be limited in their effectiveness against evolving threats. This work presents a novel framework for real-time threat detection and visualisation leveraging Natural Language Processing (NLP) and Knowledge Graphs (KG). By combining these techniques, this approach connects threat patterns and entities, enabling a more detailed understanding of cyber-attacks. Considering the ever-increasing amount of data and communications, this work demonstrates improvements in detection accuracy as well as the ability to visualise complex attack patterns using the proposed framework, leading to a deeper understanding of cyber-security operations. The proposed framework has been validated using real-world datasets, demonstrating its effectiveness in predicting and mitigating threats better than traditional systems. The study provides new insights into the growing role of NLP and KGs in modern cybersecurity environments.

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