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

TL;DR

The paper tackles security vulnerabilities in PoS blockchains arising from their open, decentralized nature. It introduces MRL-PoS+, a multi-agent reinforcement learning–based consensus mechanism with a penalty-reward framework to detect and mitigate malicious nodes without centralized authority. A proof-of-concept demonstrates effectiveness against six major attacks and shows improved attack resilience without added computational overhead compared to traditional PoS schemes. The work advances decentralized, scalable defenses for PoS networks by integrating reputation-based validator selection and adaptive penalties to sustain network integrity.

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.

Figures (5)

• Figure 1: System Architecture of MRL-PoS+
• Figure 2: Penalty Reward Mechanism
• Figure 3: Elimination of Malicious Nodes for Different Attacks
• Figure 4: Learning Stages of a Regular Node
• Figure 5: Comparison with Regular PoS and DPoS