Table of Links Abstract and 1. Introduction 2 Related Work and 2.1 Technology Convergence Approaches 2.2 Technology Convergence Measurements 2.3 Technology Convergence Models 3 Data 4 Method and 4.1 Proximity Indices 4.2 Interpolation and Fitting Data 4.3 Clustering 4.4 Forecasting 5 Results and Discussion and 5.1 Overall Results 5.2 Case Study 5.3 Limitations and Future Works 6 Conclusion and References Appendix Abstract Identifying technological convergence among emerging technologies in cybersecurity is a crucial task for advancing science and fostering innovation. Unlike previous studies that focus on the binary relationship between a paper and the concept it attributes to technology, our approach utilizes attribution scores to enhance the relationships between research papers, combining keywords, citation rates, and collaboration status with specific technological concepts. The proposed method integrates text mining and bibliometric analyses to formulate and predict technological proximity indices for encryption technologies using the ’OpenAlex’ catalog. Our case study findings highlight a significant convergence between blockchain and public-key cryptography, evident in the increasing proximity indices. These results offer valuable strategic insights for those contemplating investments in these domains. 1 Introduction In an era characterized by a technological revolution, understanding the dynamics of technological evolution, convergence, and emergence has become crucial for advancing science and fostering economic innovation [1–4]. While emergent technologies continue to reshape global landscapes socially, economically, and intellectually [5], a substantial gap exists in the literature concerning a comprehensive quantitative measure for assessing technological convergence [6, 7]. To address this critical gap, our study employs the integration of bibliometric indicators, such as collaboration, common keywords, and citations, to facilitate a multidimensional analysis of technological convergence. We leverage the OpenAlex database, a rich source of scholarly papers, to model the evolution of encryption technology from 2002 to 2022. Unlike previous studies that typically use the binary relationship between research and the concept attribution to a specific technology, we leverage OpenAlex’s technology attribution scores. This enhances the relational granularity between research papers and specific technological concepts, thus improving the accuracy of identifying technological convergence. Additionally, we use random forests to generate time series of proximity indices to forecast technological trajectories. This approach has allowed us to identify a significant convergence between blockchain and public-key cryptography, aligning well with the trend in the growing practical application of public-key cryptography within blockchain ecosystems. By identifying early stages of technological convergence, our study not only complements but also extends traditional patent analyses, providing better insight into emerging technological trends. This foresight can guide strategic investment and development in cybersecurity to strengthen defenses against evolving cyber threats. The paper is organized as follows. In Section 2, we evaluate the strengths and weaknesses of existing literature. Section 3 outlines the data processing methodology, and Section 4 presents the details of our proposed model. The results and their interpretations are presented in Section 5. Finally, Section 6 discusses the limitations of our study, potential extensions, and offers concluding remarks. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work and 2.1 Technology Convergence Approaches 2 Related Work and 2.1 Technology Convergence Approaches 2.2 Technology Convergence Measurements 2.2 Technology Convergence Measurements 2.3 Technology Convergence Models 2.3 Technology Convergence Models 3 Data 3 Data 4 Method and 4.1 Proximity Indices 4 Method and 4.1 Proximity Indices 4.2 Interpolation and Fitting Data 4.2 Interpolation and Fitting Data 4.3 Clustering 4.3 Clustering 4.4 Forecasting 4.4 Forecasting 5 Results and Discussion and 5.1 Overall Results 5 Results and Discussion and 5.1 Overall Results 5.2 Case Study 5.2 Case Study 5.3 Limitations and Future Works 5.3 Limitations and Future Works 6 Conclusion and References 6 Conclusion and References Appendix Appendix Abstract Identifying technological convergence among emerging technologies in cybersecurity is a crucial task for advancing science and fostering innovation. Unlike previous studies that focus on the binary relationship between a paper and the concept it attributes to technology, our approach utilizes attribution scores to enhance the relationships between research papers, combining keywords, citation rates, and collaboration status with specific technological concepts. The proposed method integrates text mining and bibliometric analyses to formulate and predict technological proximity indices for encryption technologies using the ’OpenAlex’ catalog. Our case study findings highlight a significant convergence between blockchain and public-key cryptography, evident in the increasing proximity indices. These results offer valuable strategic insights for those contemplating investments in these domains. cybersecurity 1 Introduction In an era characterized by a technological revolution, understanding the dynamics of technological evolution, convergence, and emergence has become crucial for advancing science and fostering economic innovation [1–4]. While emergent technologies continue to reshape global landscapes socially, economically, and intellectually [5], a substantial gap exists in the literature concerning a comprehensive quantitative measure for assessing technological convergence [6, 7]. To address this critical gap, our study employs the integration of bibliometric indicators, such as collaboration, common keywords, and citations, to facilitate a multidimensional analysis of technological convergence. We leverage the OpenAlex database, a rich source of scholarly papers, to model the evolution of encryption technology from 2002 to 2022. Unlike previous studies that typically use the binary relationship between research and the concept attribution to a specific technology, we leverage OpenAlex’s technology attribution scores. This enhances the relational granularity between research papers and specific technological concepts, thus improving the accuracy of identifying technological convergence. Additionally, we use random forests to generate time series of proximity indices to forecast technological trajectories. This approach has allowed us to identify a significant convergence between blockchain and public-key cryptography, aligning well with the trend in the growing practical application of public-key cryptography within blockchain ecosystems. By identifying early stages of technological convergence, our study not only complements but also extends traditional patent analyses, providing better insight into emerging technological trends. This foresight can guide strategic investment and development in cybersecurity to strengthen defenses against evolving cyber threats. The paper is organized as follows. In Section 2, we evaluate the strengths and weaknesses of existing literature. Section 3 outlines the data processing methodology, and Section 4 presents the details of our proposed model. The results and their interpretations are presented in Section 5. Finally, Section 6 discusses the limitations of our study, potential extensions, and offers concluding remarks. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland. Authors: Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland.
