Cybersecurity Workforce Upskilling: CRISP-DM Automation with Jupyter Notebooks and Immersive Lab Terraform Modules
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Information Security | United States of America | Volume 14 Issue 4, April 2025 | Popularity: 5 / 10


     

Cybersecurity Workforce Upskilling: CRISP-DM Automation with Jupyter Notebooks and Immersive Lab Terraform Modules

Sandhya Guduru


Abstract: The rapid evolution of cybersecurity threats necessitates continuous workforce upskilling, with automation playing a crucial role in enhancing training effectiveness. This research explores the integration of the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework with Jupyter Notebooks for automating threat modeling and security analysis. Additionally, it examines the use of Immersive Lab Terraform modules to provide hands-on cloud attack simulations, bridging the gap between theoretical knowledge and practical application. By aligning these methodologies with CyberSeek Heatmap metrics, this approach ensures targeted skill development that meets industry demands. The study highlights the potential of automation-driven training pipelines in equipping cybersecurity professionals with the necessary expertise to mitigate evolving threats.


Keywords: Cybersecurity Workforce Upskilling, CRISP-DM, Jupyter Notebooks, Threat Modeling, Immersive Lab Terraform Modules, Cloud Security, CyberSeek Heatmap, Security Automation, Hands-on Training, Skill Gap Analysis


Edition: Volume 14 Issue 4, April 2025


Pages: 907 - 911


DOI: https://www.doi.org/10.21275/SR25408010102


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Sandhya Guduru, "Cybersecurity Workforce Upskilling: CRISP-DM Automation with Jupyter Notebooks and Immersive Lab Terraform Modules", International Journal of Science and Research (IJSR), Volume 14 Issue 4, April 2025, pp. 907-911, https://www.ijsr.net/getabstract.php?paperid=SR25408010102, DOI: https://www.doi.org/10.21275/SR25408010102

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