SECURITY CONVERGENCE IN INDUSTRY 5.0: LESSONS FROM GAME ANTI-CHEAT SYSTEMS FOR DIGITAL TWIN PROTECTION IN COMPUTER SYSTEMS

Abstract

Digital Twin (DT) systems are critical to the advancement of Industry 5.0, enabling synchronized, intelligent modeling of physical assets for simulation, monitoring, and predictive control. However, these platforms' growing integration of AI, telemetry data, and autonomous decision-making exposes them to escalating cybersecurity threats. This study explores how established security practices from the video game industry—specifically anti-cheat technologies—can be repurposed to address the evolving security demands of DTs.
We conducted a comparative literature review and architecture mapping between video game environments and DT infrastructures, focusing on behavioral spoofing, telemetry injection, and runtime tampering. Additionally, we performed simulations using statistical and machine learning models (Z-score filters, SVM, LSTM) to assess the adaptability of game-based detection mechanisms.
Results show that AI-assisted behavioral modeling significantly enhances threat detection accuracy while maintaining low latency. We propose a layered, privacy-conscious cybersecurity framework for digital twins based on these findings. This research demonstrates that the convergence of anti-cheat systems and computer engineering offers a viable strategy for building resilient and ethically aligned digital infrastructure in the Industry 5.0 era.