Enabling Cyber Security Education through Digital Twins and Generative AI
Vita Santa Barletta, Vito Bavaro, Miriana Calvano, Antonio Curci, Antonio Piccinno, Davide Pio Posa

TL;DR
This paper explores how integrating Digital Twins with penetration testing tools and Large Language Models can create interactive, realistic cybersecurity training environments that enhance learning and operational readiness.
Contribution
It introduces a novel framework combining Digital Twins, a custom penetration testing toolkit, and LLMs for immersive cybersecurity education and training.
Findings
Improved training effectiveness and engagement.
Enhanced real-time feedback and threat explanation.
Bridging the gap between theory and practice in cybersecurity.
Abstract
Digital Twins (DTs) are gaining prominence in cybersecurity for their ability to replicate complex IT (Information Technology), OT (Operational Technology), and IoT (Internet of Things) infrastructures, allowing for real time monitoring, threat analysis, and system simulation. This study investigates how integrating DTs with penetration testing tools and Large Language Models (LLMs) can enhance cybersecurity education and operational readiness. By simulating realistic cyber environments, this approach offers a practical, interactive framework for exploring vulnerabilities and defensive strategies. At the core of this research is the Red Team Knife (RTK), a custom penetration testing toolkit aligned with the Cyber Kill Chain model. RTK is designed to guide learners through key phases of cyberattacks, including reconnaissance, exploitation, and response within a DT powered ecosystem. The…
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Taxonomy
TopicsDigital Transformation in Industry · Scientific Computing and Data Management · Computational Physics and Python Applications
