Advanced Threat Pattern Recognition and Visualization in Cybersecurity using Knowledge Graphs and Natural Language Processing

Abstract

The growing sophistication and frequency of cyber threats call for new methods to detect, analyze, and respond. Many of the traditional cybersecurity approaches are known to leave gaps as they fail to factor in or address dynamic and interconnected cyber-attacks prevalent today. This thesis proposes a new approach to utilizing NLP and Knowledge Graphs to improve the detection of cyber threats as well as graphical representation. We convert structured data into sentences to extract entity pairs and relationships for producing a full knowledge graph. Sixty unique threat schemas are described and then used to match with other schemata in a graph as similar, for the accurate discovery of threats. Our method is empirically analyzed by using network graphs, stacked bar charts, pie of pies, and heat maps which show patterns that the methods effectively detect the most frequent threats with high distribution and severity. This approach offers improved situational awareness, and faster threat detection and enables evidence-driven decision-making in cybersecurity operations. This research intends to significantly enhance the accuracy and efficacy of cyber threat management, by overcoming the shortcomings of traditional methods. The results demonstrate the promise of coupling Knowledge Graphs with NLP for building effective, scalable, and adaptive cybersecurity solutions that can significantly improve threat mitigation strategies.