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

TL;DR

This paper introduces MRL-PoS+, a novel multi-agent reinforcement learning-based consensus mechanism that enhances PoS blockchain security by detecting and penalizing malicious nodes, effectively resisting major attacks without extra computational costs.

Contribution

It presents a new PoS consensus algorithm leveraging MRL to detect and eliminate malicious nodes, improving security against attacks in a decentralized blockchain environment.

Findings

Effectively eliminates malicious nodes in six attack types

Significantly improves attack resilience over traditional schemes

Operates without additional computational overhead

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.