MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for
Metaverse based on Blockchain and Online Federated Learning
Abstract
Metaverse is envisioned to operate on top of the Internet of Things
(IoT) in which numerous IoT sensors and wearable devices continuously
collect real-world data to reflect the physical world into its virtual
counterpart. As a result, the metaverse not only inherits various
security threats from the IoT network, but also faces far more
sophisticated attacks related to virtual reality technology. As
traditional security approaches show various limitations in the
large-scale distributed metaverse such as single point of failure (SPoF)
and scalability issue, this paper proposes MetaCIDS, a novel
collaborative intrusion detection (CID) framework that leverages
metaverse devices to collaboratively protect the metaverse. In MetaCIDS,
a federated learning (FL) scheme based on unsupervised autoencoder and
an attention-based supervised classifier enables metaverse users to
train a CID model using their local network data, while the blockchain
network allows metaverse users to utilize an on-chain model to detect
intrusion network flows over their monitored local network traffic, then
submit verifiable intrusion alerts to the blockchain to earn metaverse
tokens. Security analysis shows that MetaCIDS can efficiently detect
zero-day attacks (i.e., attacks unseen in the training data), while the
training process is resistant to SPoF, data tampering, and up to 33%
poisoning nodes thank to the blockchain consensus, reputation, and
incentive mechanisms. Performance evaluation illustrates the efficiency
of MetaCIDS with 96% to 99% detection accuracy on four different
network intrusion datasets, supporting both multi-class detection using
labeled data and anomaly detection trained on unlabeled data.