Blockchain-Inspired Collaborative Cyber-Attacks Detection for Securing Metaverse
- Authors
- Zainudin, Ahmad; Putra, Made Adi Paramartha; Alief, Revin Naufal; Akter, Rubina; Kim, Dong-Seong; Lee, Jae-Min
- Issue Date
- May-2024
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Metaverse; Blockchains; Training; Reliability; Federated learning; Internet of Things; Cyberattack; ERC-20-based token incentive mechanism; federated intrusion detection system (FIDS); hybrid client selection (HCS); trusted decentralized aggregation
- Citation
- IEEE INTERNET OF THINGS JOURNAL, v.11, no.10, pp 18221 - 18236
- Pages
- 16
- Journal Title
- IEEE INTERNET OF THINGS JOURNAL
- Volume
- 11
- Number
- 10
- Start Page
- 18221
- End Page
- 18236
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28802
- DOI
- 10.1109/JIOT.2024.3364247
- ISSN
- 2372-2541
2327-4662
- 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.
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Collections - School of Electronic Engineering > 1. Journal Articles
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