Artificial Intelligence and Hybrid Threats in Defense and Security: A Bibliometric and Cluster Analysis of the Scientific Literature (2000–2025)
DOI:
https://doi.org/10.18041/1900-0642/criteriolibre.2025v23n43.13701Keywords:
Artificial Inteligencie (AI), hybrid threats, bibliometric analysis, clusters, machine learningAbstract
National security faces a strategic challenge arising from the complex and accelerated convergence between Artificial Intelligence (AI) and hybrid threats. In response to the need to deepen and systematize knowledge in this domain, a systematic mapping of a rapidly evolving field was conducted through the application of a quantitative methodology based on bibliometric and cluster analysis. The study examined 20,000 documents retrieved from the Scopus database over the period 2000-2025. The coherence of the hierarchical clustering model was validated using the Calinski-Harabasz index (1671,883), ensuring a clear differentiation between groups. The results reveal thematic maturity characterized by exponential growth, reaching its peak in 2023 with approximately 9,000 documents. A structure dominated by four interconnected cores was identified, envolving from traditional cybersecurity and machine learning towards ethics, privacy, and critical applications (5G/IoT). The analysis of the ten clusters confirmed a clear core-periphery structure of knowledge, where the primary cluster concentrates the critical mass of publications. Geographically, scientific output is hegemonically concentrated in India, China, and the United States. In conclusion, research on AI and security has envolved toward multidisciplinarity, highlighting the urgency of integrating ethical governance and technological resilience considerations into the formulation of national defense policies in the face of automation of hybrid threats.
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