M. Papoutsoglou, G. Meditskos, N. Bassiliades, E. Kontopoulos, S. Vrochidis, “Mapping the Current Status of CTI Knowledge Graphs through a Bibliometric Analysis”, AIKGC2024: AI-Driven Knowledge Graph Construction, Workshop at SETN 2024, Sep 11-13, 2024, Athens, Greece, Proc. of the 13th Hellenic Conference on Artificial Intelligence (SETN '24), Association for Computing Machinery, New York, NY, USA, Article 42, 1-6.
Bibliometric analysis in the field of cybersecurity and Cyber Threat Intelligence (CTI) is crucial for identifying research trends, key themes, and collaborative networks, which can guide future research directions and policy decisions. This paper presents a comprehensive bibliometric analysis of the current status of research on knowledge graphs in cybersecurity, highlighting significant trends and thematic clusters. The analysis reveals a rapidly growing interest in integrating knowledge graphs with advanced machine learning and AI techniques, such as deep learning and neural networks, to enhance cyber threat intelligence and response strategies. Key findings include the prominence of natural language processing, entity recognition, and relation extraction as critical methodologies in this field. Thematic evolution analysis shows the adoption of large language models (LLMs) and an ongoing focus on structured knowledge representation. The study underscores the potential of knowledge graphs to improve cybersecurity through better data organization, threat detection, and intelligence extraction.
