Securing Proof of Stake Blockchains: Leveraging Multi-Agent Reinforcement Learning for Detecting and Mitigating Malicious Nodes

• URL: http://arxiv.org/abs/2407.20983v1
• Date: Tue, 30 Jul 2024 17:18:03 GMT
• Title: Securing Proof of Stake Blockchains: Leveraging Multi-Agent Reinforcement Learning for Detecting and Mitigating Malicious Nodes
• Authors: Faisal Haque Bappy, Kamrul Hasan, Md Sajidul Islam Sajid, Mir Mehedi Ahsan Pritom, Tariqul Islam,
• Abstract summary: MRL-PoS+ is a novel consensus algorithm to enhance the security of PoS blockchains. We show that MRL-PoS+ significantly improves the attack resilience of PoS blockchains.
• Score: 0.2982610402087727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Proof of Stake (PoS) blockchains offer promising alternatives to traditional Proof of Work (PoW) systems, providing scalability and energy efficiency. However, blockchains operate in a decentralized manner and the network is composed of diverse users. This openness creates the potential for malicious nodes to disrupt the network in various ways. Therefore, it is crucial to embed a mechanism within the blockchain network to constantly monitor, identify, and eliminate these malicious nodes without involving any central authority. In this paper, we propose MRL-PoS+, a novel consensus algorithm to enhance the security of PoS blockchains by leveraging Multi-agent Reinforcement Learning (MRL) techniques. Our proposed consensus algorithm introduces a penalty-reward scheme for detecting and eliminating malicious nodes. This approach involves the detection of behaviors that can lead to potential attacks in a blockchain network and hence penalizes the malicious nodes, restricting them from performing certain actions. Our developed Proof of Concept demonstrates effectiveness in eliminating malicious nodes for six types of major attacks. Experimental results demonstrate that MRL-PoS+ significantly improves the attack resilience of PoS blockchains compared to the traditional schemes without incurring additional computation overhead.

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