Blockchain-Inspired Collaborative Cyber-Attacks Detection for Securing Metaverse
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zainudin, Ahmad | - |
dc.contributor.author | Putra, Made Adi Paramartha | - |
dc.contributor.author | Alief, Revin Naufal | - |
dc.contributor.author | Akter, Rubina | - |
dc.contributor.author | Kim, Dong-Seong | - |
dc.contributor.author | Lee, Jae-Min | - |
dc.date.accessioned | 2024-07-19T02:30:26Z | - |
dc.date.available | 2024-07-19T02:30:26Z | - |
dc.date.issued | 2024-05 | - |
dc.identifier.issn | 2372-2541 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28802 | - |
dc.description.abstract | The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-attacks. To prevent and mitigate malicious network activities in a distributed metaverse, conventional intrusion detection systems (IDS) have communication overhead and privacy concerns. Federated learning (FL) techniques are widely employed to develop IDS frameworks and enable privacy-preserving collaborative learning schemes in decentralized ecosystems. However, the vanilla FL system utilizes a centralized FL aggregation technique, which introduces a single point of failure (SPoF) and potential unauthorized aggregators, allowing malicious clients to inject false data parameters, known as poisoning attacks. Furthermore, low-quality clients in the FL system can result in degraded model performance and hinder convergence. This study proposes a secure and reliable blockchain-aided federated learning (BFL)-based IDS framework using a lightweight model for securing metaverse. An authorized federated IDS is proposed to establish a trustworthy decentralized aggregation mechanism, utilizing Proof-of-Authority (PoA) consensus. The proposed federated IDS implemented a hybrid client selection (HCS) technique, considering the accuracy and reputation of client histories, to select high-quality metaverse edge devices. Additionally, a fairness ERC-20 token-based incentive mechanism was developed to reward selected FL clients as a token of appreciation for their contribution to the FL training processes. According to the IDS framework measurements, the proposed model performs better than the existing approaches for detecting cyber-attacks in metaverse environments, achieving an accuracy of 99.28% with trainable parameters of 1.8K and mega floating-point operations (MFLOPs) of 0.0016. | - |
dc.format.extent | 16 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Blockchain-Inspired Collaborative Cyber-Attacks Detection for Securing Metaverse | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/JIOT.2024.3364247 | - |
dc.identifier.scopusid | 2-s2.0-85187282197 | - |
dc.identifier.wosid | 001221337300117 | - |
dc.identifier.bibliographicCitation | IEEE INTERNET OF THINGS JOURNAL, v.11, no.10, pp 18221 - 18236 | - |
dc.citation.title | IEEE INTERNET OF THINGS JOURNAL | - |
dc.citation.volume | 11 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 18221 | - |
dc.citation.endPage | 18236 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordAuthor | Metaverse | - |
dc.subject.keywordAuthor | Blockchains | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Reliability | - |
dc.subject.keywordAuthor | Federated learning | - |
dc.subject.keywordAuthor | Internet of Things | - |
dc.subject.keywordAuthor | Cyberattack | - |
dc.subject.keywordAuthor | ERC-20-based token incentive mechanism | - |
dc.subject.keywordAuthor | federated intrusion detection system (FIDS) | - |
dc.subject.keywordAuthor | hybrid client selection (HCS) | - |
dc.subject.keywordAuthor | trusted decentralized aggregation | - |
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