Open Access

A Longitudinal Analysis of Cybersecurity Technology and Innovation: A Technology Mining Approach Using Bibliometric and Patent Analysis

4 Department of Information Systems, Global School of Business and Technology, Madrid, Spain

Abstract

Background: The field of cybersecurity R&D has experienced exponential growth, driven by the escalating complexity and frequency of cyber threats. Understanding the evolving landscape of this domain is critical for strategic planning, resource allocation, and maintaining national security. This study addresses a significant gap in the literature by providing a comprehensive, longitudinal analysis that integrates both academic research and technological innovation.

Methods: This research employs a technology mining approach combining bibliometric and patent analyses with social network mapping. Data was systematically collected from major academic and patent databases from 1999 to the present. We used co-citation analysis to map the intellectual structure of the field, and patent citation analysis and h-index to identify influential corporate innovators. Social network analysis was applied to visualize collaboration patterns and pinpoint key actors.

Results: Our findings confirm an exponential annual growth rate of 19.7% in cybersecurity R&D. The U.S. leads in both academic output and patent filings, with notable contributions from Europe and Asia in specific areas like smart grids and cyber-physical security. The analysis identified deep learning, blockchain deterrence, human cybersecurity behavior, and supply chain security as dominant emerging research clusters. IBM stands out as the most influential corporate player, holding 11,652 patent citations and an h-index of 78, followed by Microsoft and Cisco.

Discussion: The study reveals a strong and dynamic interplay between academic and industrial innovation. The identified emerging themes represent critical future priorities for investment and R&D. The combined methodology offers a valuable foresight tool for policymakers, R&D managers, and investors. The results provide actionable insights for steering future research and development efforts to combat next-generation cyber threats.

Keywords

References

📄 Daim, T., Yalcin, H., Mermoud, A., et al. (2024). Exploring cybertechnology standards through bibliometrics: case of National Institute of Standards And Technology. World Patent Inf., 77, 102278. [https://doi.org/10.1016/j.wpi.2024.102278](https://www.google.com/search?q=https://doi.org/10.1016/j.wpi.2024.102278)
📄 Yalcin, H., Daim, T., Moughari, M. M., et al. (2024). Supercomputers and quantum computing on the axis of cyber security. Technol. Soc., 77, 102556. [https://doi.org/10.1016/j.techsoc.2024.102556](https://www.google.com/search?q=https://doi.org/10.1016/j.techsoc.2024.102556)
📄 Fujita, K., Kajikawa, Y., Mori, J., et al. (2014). Detecting research fronts using different types of weighted citation networks. J. Eng. Technol. Manag., 32, 129–146. [https://doi.org/10.1016/j.jengtecman.2013.07.002](https://doi.org/10.1016/j.jengtecman.2013.07.002)
📄 Herrmann, H. (2022). The arcanum of artificial intelligence in enterprise applications: toward a unified framework. J. Eng. Technol. Manag., 66, 101716. [https://doi.org/10.1016/j.jengtecman.2022.101716](https://www.google.com/search?q=https://doi.org/10.1016/j.jengtecman.2022.101716)
📄 Díaz-Garrido, E., Pinillos, M.-J., Soriano-Pinar, I., et al. (2018). Changes in the intellectual basis of servitization research: a dynamic analysis. J. Eng. Technol. Manag., 48, 1–14. [https://doi.org/10.1016/j.jengtecman.2018.01.005](https://www.google.com/search?q=https://doi.org/10.1016/j.jengtecman.2018.01.005)
📄 Martín-Peña, M. L., Pinillos, M.-J., & Reyes, L.-E. (2017). The intellectual basis of servitization: a bibliometric analysis. J. Eng. Technol. Manag., 43, 83–97. [https://doi.org/10.1016/j.jengtecman.2017.01.005](https://www.google.com/search?q=https://doi.org/10.1016/j.jengtecman.2017.01.005)
📄 Pitt, C., Park, A., & McCarthy, I. P. (2021). A bibliographic analysis of 20 years of research on innova-tion and new product development in technology and innovation management (TIM) journals. J. Eng. Technol. Manag., 61, 101632. [https://doi.org/10.1016/j.jengtecman.2021.101632](https://doi.org/10.1016/j.jengtecman.2021.101632)
📄 Soranzo, B., Nosella, A., & Filippini, R. (2016). Managing firm patents: a bibliometric investigation into the state of the art. J. Eng. Technol. Manag., 42, 15–30. [https://doi.org/10.1016/j.jengtecman.2016.08.002](https://doi.org/10.1016/j.jengtecman.2016.08.002)
📄 Naeini, B. A., Zamani, M., Daim, T. U., et al. (2022). Conceptual structure and perspectives on “innovation management”: a bibliometric review. Technol. Forecast. Soc. Change, 185, 122052. [https://doi.org/10.1016/j.techfore.2022.122052](https://www.google.com/search?q=https://doi.org/10.1016/j.techfore.2022.122052)
📄 Yalcin, H., & Daim, T. (2021). A scientometric review of technology capability research. J. Eng. Technol. Manag., 62, 101658. [https://doi.org/10.1016/j.jengtecman.2021.101658](https://doi.org/10.1016/j.jengtecman.2021.101658)
📄 Ittipanuvat, V., Fujita, K., Sakata, I., et al. (2014). Finding linkage between technology and social issue: a literature based discovery approach. J. Eng. Technol. Manag., 32, 160–184. [https://doi.org/10.1016/j.jengtecman.2013.05.006](https://doi.org/10.1016/j.jengtecman.2013.05.006)
📄 Yang, H., & Jung, W.-S. (2016). Structural dynamics of keyword networks: liquid crystal display and plasma display panel cases. J. Eng. Technol. Manag., 40, 64–75. [https://doi.org/10.1016/j.jengtecman.2016.04.002](https://www.google.com/search?q=https://doi.org/10.1016/j.jengtecman.2016.04.002)
📄 Newman, N. C., Porter, A. L., Newman, D., et al. (2014). Comparing methods to extract technical content for technological intelligence. J. Eng. Technol. Manag., 32, 97–109. [https://doi.org/10.1016/j.jengtecman.2013.09.001](https://doi.org/10.1016/j.jengtecman.2013.09.001)
📄 Frenken, K., Hölzl, W., & de Vor, F. (2005). The citation impact of research collaborations: the case of European biotechnology and applied microbiology (1988–2002). J. Eng. Technol. Manag., 22(1–2), 9–30. [https://doi.org/10.1016/j.jengtecman.2004.11.002](https://doi.org/10.1016/j.jengtecman.2004.11.002)
📄 Cunningham, S. W., & Kwakkel, J. H. (2014). Tipping points in science: a catastrophe model of scientific change. J. Eng. Technol. Manag., 32, 185–205. [https://doi.org/10.1016/j.jengtecman.2014.01.002](https://doi.org/10.1016/j.jengtecman.2014.01.002)
📄 Jeong, K., Noh, H., Song, Y.-K., et al. (2017). Essential patent portfolios to monitor technology standardization strategies: case of LTE-A technologies. J. Eng. Technol. Manag., 45, 18–36. [https://doi.org/10.1016/j.jengtecman.2017.07.001](https://www.google.com/search?q=https://doi.org/10.1016/j.jengtecman.2017.07.001)
📄 Cammarano, A., Varriale, V., Michelino, F., et al. (2023). The importance of possessing knowledge on black-box components: the case of smartphone OEMs. J. Eng. Technol. Manag., 67, 101727. [https://doi.org/10.1016/j.jengtecman.2022.101727](https://www.google.com/search?q=https://doi.org/10.1016/j.jengtecman.2022.101727)
📄 Giglio, C. (2021). Cross-country creativity and knowledge flows of patent acquisitions: drivers and implications for managers and policymakers. J. Eng. Technol. Manag., 59, 101617. [https://doi.org/10.1016/j.jengtecman.2021.101617](https://doi.org/10.1016/j.jengtecman.2021.101617)
📄 Huang, J. (2016). Patent portfolio analysis of the cloud computing industry. J. Eng. Technol. Manag., 39, 45–64. [https://doi.org/10.1016/j.jengtecman.2016.01.002](https://doi.org/10.1016/j.jengtecman.2016.01.002)
📄 Jeong, S., & Lee, S. (2015). What drives technology convergence? Exploring the influence of technological and resource allocation contexts. J. Eng. Technol. Manag., 36, 78–96. [https://doi.org/10.1016/j.jengtecman.2015.05.004](https://doi.org/10.1016/j.jengtecman.2015.05.004)
📄 Lai, Y., & Che, H.-C. (2009). Evaluating patents using damage awards of infringement lawsuits: a case study. J. Eng. Technol. Manag., 26(3), 167–180. [https://doi.org/10.1016/j.jengtecman.2009.06.005](https://doi.org/10.1016/j.jengtecman.2009.06.005)
📄 Levitas, E. F., McFadyen, M. A., & Loree, D. (2006). Survival and the introduction of new technology: a patent analysis in the integrated circuit industry. J. Eng. Technol. Manag., 23(3), 182–201. [https://doi.org/10.1016/j.jengtecman.2006.06.008](https://doi.org/10.1016/j.jengtecman.2006.06.008)
📄 Roepke, S., & Moehrle, M. G. (2014). Sequencing the evolution of technologies in a system-oriented way: the concept of technology-dna. J. Eng. Technol. Manag., 32, 110–128. [https://doi.org/10.1016/j.jengtecman.2013.08.005](https://doi.org/10.1016/j.jengtecman.2013.08.005)
📄 Zhang, H., Daim, T., & Zhang, Y. (2021). Integrating patent analysis into technology roadmapping: a latent dirichlet allocation-based technology assessment and roadmapping in the field of blockchain. Technol. Forecast. Soc. Change, 167. [https://doi.org/10.1016/j.techfore.2021.120729](https://doi.org/10.1016/j.techfore.2021.120729)
📄 Li, S., Garces, E., & Daim, T. (2019). Technology forecasting by analogy-based on social network analysis: the case of autonomous vehicles. Technol. Forecast. Soc. Change, 148, 119731. [https://doi.org/10.1016/j.techfore.2019.119731](https://doi.org/10.1016/j.techfore.2019.119731)
📄 Zeba, G., Dabić, M., Čičak, M., et al. (2021). Technology mining: artificial intelligence in manufacturing. Technol. Forecast. Soc. Change, 171, 120971. [https://doi.org/10.1016/j.techfore.2021.120971](https://doi.org/10.1016/j.techfore.2021.120971)
📄 Daim, T., Lai, K. K., Yalcin, H., et al. (2020). Forecasting technological positioning through technol-ogy knowledge redundancy: patent citation analysis of IoT, cybersecurity, and Blockchain. Technol. Forecast. Soc. Change, 161, 120329. [https://doi.org/10.1016/j.techfore.2020.120329](https://doi.org/10.1016/j.techfore.2020.120329)
📄 Gonçalves Pereira, C., Ricardo Lavoie, J., & Garces, E., et al. (2019). Forecasting of emerging therapeutic monoclonal antibodies patents based on a decision model. Technol. Forecast. Soc. Change, 139, 185–199. [https://doi.org/10.1016/j.techfore.2018.11.002](https://www.google.com/search?q=https://doi.org/10.1016/j.techfore.2018.11.002)
📄 Li, X., Wu, Y., Cheng, H., et al. (2023). Identifying technology opportunity using SAO semantic mining and outlier detection method: a case of triboelectric nanogenerator technology. Technol. Forecast. Soc. Change, 189, 122353. [https://doi.org/10.1016/j.techfore.2023.122353](https://www.google.com/search?q=https://doi.org/10.1016/j.techfore.2023.122353)
📄 Lai, K., Chen, Y.-L., Kumar, V., et al. (2023). Mapping technological trajectories and exploring knowledge sources: a case study of E-payment technologies. In: Technological forecasting and social change, 186. [https://doi.org/10.1016/j.techfore.2022.122173](https://doi.org/10.1016/j.techfore.2022.122173)
📄 Li, X., Xie, Q., Daim, T., et al. (2019). Forecasting technology trends using text mining of the gaps between science and technology: the case of perovskite solar cell technology. Technol. Forecast. Soc. Change, 146, 432–449. [https://doi.org/10.1016/j.techfore.2019.01.012](https://doi.org/10.1016/j.techfore.2019.01.012)
📄 Li, S., Zhang, X., Xu, H., et al. (2020). Measuring strategic technological strength: patent portfolio Model. Technol. Forecast. Soc. Change, 157, 120119. [https://doi.org/10.1016/j.techfore.2020.120119](https://doi.org/10.1016/j.techfore.2020.120119)
📄 Li, X., Wen, Y., Jiang, J., et al. (2022). Identifying potential breakthrough research: a machine learning method using scientific papers and twitter data. Technol. Forecast. Soc. Change, 184, 122042. [https://doi.org/10.1016/j.techfore.2022.122042](https://doi.org/10.1016/j.techfore.2022.122042)

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