Earticle

초록

영어

Metaverse, our virtual reality, is traversed via an Avatar linked to a user profile through Personal Identifiable Information (PII). To secure this PII from causing attacker infiltration, only authorised users should access these avatars permitted by the authentication systems. The authentication systems are researched to be resilient against attackers’ manipulations. These systems rely on dynamic and real-time sensor data rather than static information from the user for authentication. Dynamic sensor data captured through Head Mounted Displays (HMDs) is highly classifiable with Machine learning (ML) and Deep Learning (DL) algorithms. Over time, the model training requires an upgrade through evolution in data processing and learning. Self-learning —Adaptive learning can lead this system to transform with its learn-evolve-adapt learning strategy. Therefore, our study attempts to explore authentication systems developed for HMDs, capturing realtime dynamic sensor data. With its results, we concluded that these systems are highly sensitive while processing the sensor data. We list out the risk factors of utilising adaptive learning for an authentication system based on neurometric data combined with biometric data. This study will be the state of the art for the self-learning algorithms for biometric and neurometric data-based authentication systems.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. NON-INVASIVE BCI-POWERED ADAPTIVE AUTHENTICATION SYSTEM FRAMEWORK
IV. EVALUATION
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Arpita Dinesh Sarang [ SysCore Lab, Department of Information Security, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, South Korea ]
• Sang-Hoon Choi [ SysCore Lab, Sejong University, Seoul 05006, South Korea ]
• Ki-Woong Park [ Department of Information Security, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, South Korea ] Corresponding Author

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