MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning
Metaverse is expected to rely on massive Internet of Things (IoT) connections so it inherits various security threats from the IoT network and also faces other sophisticated attacks related to virtual reality technology. As traditional security approaches show various limitations in the large-scale...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of the Computer Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10239541/ |
| _version_ | 1797682024760213504 |
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| author | Vu Tuan Truong Long Bao Le |
| author_facet | Vu Tuan Truong Long Bao Le |
| author_sort | Vu Tuan Truong |
| collection | DOAJ |
| description | Metaverse is expected to rely on massive Internet of Things (IoT) connections so it inherits various security threats from the IoT network and also faces other sophisticated attacks related to virtual reality technology. As traditional security approaches show various limitations in the large-scale distributed metaverse, 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 train a machine learning (ML) 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, while the training process is resistant to SPoF, data tampering, and up to 33% poisoning nodes. 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. |
| first_indexed | 2024-03-11T23:54:32Z |
| format | Article |
| id | doaj.art-d44fb7808609469caf9531877cc8f7f2 |
| institution | Directory Open Access Journal |
| issn | 2644-1268 |
| language | English |
| last_indexed | 2024-03-11T23:54:32Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj.art-d44fb7808609469caf9531877cc8f7f22023-09-18T23:00:20ZengIEEEIEEE Open Journal of the Computer Society2644-12682023-01-01425326610.1109/OJCS.2023.331229910239541MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated LearningVu Tuan Truong0https://orcid.org/0009-0003-3072-7905Long Bao Le1https://orcid.org/0000-0003-3577-6530Institut National de la Recherche Scientifique, University of Québec, Montréal, QC, CanadaInstitut National de la Recherche Scientifique, University of Québec, Montréal, QC, CanadaMetaverse is expected to rely on massive Internet of Things (IoT) connections so it inherits various security threats from the IoT network and also faces other sophisticated attacks related to virtual reality technology. As traditional security approaches show various limitations in the large-scale distributed metaverse, 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 train a machine learning (ML) 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, while the training process is resistant to SPoF, data tampering, and up to 33% poisoning nodes. 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.https://ieeexplore.ieee.org/document/10239541/Blockchaincollaborative intrusion detectionfederated learningmetaversesemi-supervised learning |
| spellingShingle | Vu Tuan Truong Long Bao Le MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning IEEE Open Journal of the Computer Society Blockchain collaborative intrusion detection federated learning metaverse semi-supervised learning |
| title | MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning |
| title_full | MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning |
| title_fullStr | MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning |
| title_full_unstemmed | MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning |
| title_short | MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning |
| title_sort | metacids privacy preserving collaborative intrusion detection for metaverse based on blockchain and online federated learning |
| topic | Blockchain collaborative intrusion detection federated learning metaverse semi-supervised learning |
| url | https://ieeexplore.ieee.org/document/10239541/ |
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