Reputation-Based Sharding Consensus Model in Information-Centric Networking

Shi, Jia; Zeng, Xuewen; Li, Yang

doi:10.3390/electronics11050830

Cited by 2 publications

(1 citation statement)

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“…A Reputation-based Sharding Consensus model applied to information-centric networking [23] has been proposed. This consensus optimizes by using Affinity Propagation (AP) algorithms and multi-dimensional trust to divide network members into shards.…”

Section: Reputation-based Blockchain Consensusmentioning

confidence: 99%

PoIR: A Node Selection Mechanism in Reputation-Based Blockchain Consensus Using Bidirectional LSTM Regression Model

Windiatmaja,

Hanggoro,

Salman

et al. 2023

CMC

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This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation (PoIR) as an alternative to traditional Proof of Work (PoW). PoIR addresses the limitations of existing reputationbased consensus mechanisms by proposing a more decentralized and fair node selection process. The proposed PoIR consensus combines Bidirectional Long Short-Term Memory (BiLSTM) with the Network Entity Reputation Database (NERD) to generate reputation scores for network entities and select authoritative nodes. NERD records network entity profiles based on various sources, i.e., Warden, Blacklists, DShield, AlienVault Open Threat Exchange (OTX), and MISP (Malware Information Sharing Platform). It summarizes these profile records into a reputation score value. The PoIR consensus mechanism utilizes these reputation scores to select authoritative nodes. The evaluation demonstrates that PoIR exhibits higher centralization resistance than PoS and PoW. Authoritative nodes were selected fairly during the 1000-block proposal round, ensuring a more decentralized blockchain ecosystem. In contrast, malicious nodes successfully monopolized 58% and 32% of transaction processes in PoS and PoW, respectively, but failed to do so in PoIR. The findings also indicate that PoIR offers efficient transaction times of 12 s, outperforms reputation-based consensus such as PoW, and is comparable to reputation-based consensus such as PoS. Furthermore, the model evaluation shows that BiLSTM outperforms other Recurrent Neural Network models, i.e., BiGRU (Bidirectional Gated Recurrent Unit), UniLSTM (Unidirectional Long Short-Term Memory), and UniGRU (Unidirectional Gated Recurrent Unit) with 0.022 Root Mean Squared Error (RMSE). This study concludes that the PoIR consensus mechanism is more resistant to centralization than PoS and PoW. Integrating BiLSTM and NERD enhances the fairness and efficiency of blockchain applications.

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Section: Reputation-based Blockchain Consensusmentioning

confidence: 99%