Open Access

ARTICLE

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

Jauzak Hussaini Windiatmaja, Delphi Hanggoro, Muhammad Salman, Riri Fitri Sari^*

Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, Indonesia

* Corresponding Author: Riri Fitri Sari. Email:

Computers, Materials & Continua 2023, 77(2), 2309-2339. https://doi.org/10.32604/cmc.2023.041152

Received 12 April 2023; Accepted 28 June 2023; Issue published 29 November 2023

Abstract

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 reputation-based 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.

---

Keywords

---