Mapping the Current Status of CTI Knowledge Graphs through a Bibliometric Analysis

A bibliometric analysis of knowledge graphs in cybersecurity and Cyber Threat Intelligence (CTI), highlighting trends, thematic clusters, and the integration of advanced AI techniques.

M. Papoutsoglou; G. Meditskos; N. Bassiliades; Efstratios Kontopoulos; S. Vrochidis· Hellenic Conference on Artificial Intelligence· 2024· DOI 10.1145/3688671.3688738

The core problem

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.

Innovation

The bibliometric 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 identifies significant trends and thematic clusters, underscoring the potential of knowledge graphs to improve cybersecurity through better data organization, threat detection, and intelligence extraction.

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.

The authors conducted a bibliometric analysis of the research landscape on knowledge graphs in cybersecurity and CTI. The methodology likely involved querying scientific databases, extracting relevant publications, and applying bibliometric techniques such as co-occurrence analysis, thematic clustering, and trend detection. The analysis focused on identifying key themes, collaborative networks, and the evolution of research topics over time. Specific details on the dataset, search queries, and analytical tools are not provided in the abstract, but the approach aligns with standard bibliometric practices. The study emphasizes the integration of knowledge graphs with machine learning and AI techniques, and the role of natural language processing, entity recognition, and relation extraction as critical methodologies.

Why it matters

The findings highlight the increasing convergence of knowledge graphs with AI and machine learning, particularly deep learning and neural networks, to enhance CTI and response strategies. The prominence of natural language processing, entity recognition, and relation extraction suggests a methodological focus on extracting structured knowledge from unstructured threat data. The adoption of large language models (LLMs) indicates a shift towards more advanced, context-aware representations. The ongoing focus on structured knowledge representation underscores the importance of ontologies and graph-based models in cybersecurity. The study's implications guide future research directions and policy decisions, emphasizing the need for interdisciplinary collaboration and the development of robust, scalable knowledge graph frameworks for cybersecurity. The potential of knowledge graphs to improve data organization, threat detection, and intelligence extraction is significant, but challenges such as data quality, scalability, and real-time processing remain.

Who should read this

CS practitioners and researchers

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