Electric Network Frequency as Environmental Fingerprint for Metaverse Security: A Comprehensive Survey

The rapid expansion of the Metaverse presents complex security challenges, particularly in verifying virtual objects and avatars within immersive environments. Conventional authentication methods, such as passwords and biometrics, often prove inadequate in these dynamic environments, especially as essential infrastructures, such as smart grids, integrate with virtual platforms. Cybersecurity threats intensify as advanced attacks introduce fraudulent data, compromising system reliability and safety. Using the Electric Network Frequency (ENF), a naturally varying signal emitted from power grids, provides an innovative environmental fingerprint to authenticate digital twins and Metaverse entities in the smart grid. This paper provides a comprehensive survey of the ENF as an environmental fingerprint for enhancing Metaverse security, reviewing its characteristics, sensing methods, limitations, and applications in threat modeling and the CIA triad (Confidentiality, Integrity, and Availability), and presents a real-world case study to demonstrate its effectiveness in practical settings. By capturing the ENF as having a unique signature that is timestamped, this method strengthens security by directly correlating physical grid behavior and virtual interactions, effectively combating threats such as deepfake manipulations. Building upon recent developments in signal processing, this strategy reinforces the integrity of digital environments, delivering robust protection against evolving cyber–physical risks and facilitating secure, scalable virtual infrastructures.