Security for the Metaverse: Blockchain and Machine Learning Techniques for Intrusion Detection

被引:13
作者
Truong, Vu Tuan [1 ]
Le, Long Bao [1 ]
机构
[1] Univ Quebec, INRS, Montreal, PQ, Canada
来源
IEEE NETWORK | 2024年 / 38卷 / 05期
关键词
Metaverse; Blockchains; Cloud computing; Malware; Avatars; Access control; Training; security; blockchain; machine learning; federated learning; augmented and virtual reality;
D O I
10.1109/MNET.2024.3351882
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Considered to be the next-generation (NextG) Internet, the Metaverse faces various security risks inherited from its predecessor and new specialized threats. It is even more challenging to mitigate these issues in a large-scale setting with numerous wearable devices such as augmented, virtual reality (AR/VR) headsets. In this article, we aim to analyze the security aspect of the Metaverse thoroughly, focusing on blockchain and machine learning (ML) solutions. Firstly, we present a 4-layer architecture of the Metaverse and discuss potential solutions for Metaverse security based on blockchain and ML. Next, we develop a decentralized collaborative intrusion detection system (CIDS) based on blockchain and federated learning (FL) that allows such the Metaverse users to collaboratively protect this digital world. This helps solving the scalability and single-point-of-failure (SPoF) issues of traditional security approaches. Finally, we outline some key challenges and discuss future research directions for Metaverse security.
引用
收藏
页码:204 / 212
页数:9
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