Securing Majority-Attack In Blockchain Using Machine Learning And Algorithmic Game Theory: A Proof of Work

TL;DR

This paper proposes a method combining machine learning and game theory to detect and prevent majority-attacks in consortium blockchain networks involving multiple institutions.

Contribution

It introduces an intelligent monitoring system that uses supervised learning and game theory to identify collusion and prevent majority-attacks in collaborative blockchain networks.

Findings

Effective detection of collusion among stakeholders.

Prevention of majority-attacks in consortium blockchains.

Enhanced security through intelligent monitoring.

Abstract

Recently we could see several institutions coming together to create consortium based blockchain networks such as Hyperledger. Although for applications of blockchain such as Bitcoin, Litcoin, etc. the majority-attack might not be a great threat but for consortium based blockchain networks where we could see several institutions such as public, private, government, etc. are collaborating, the majority-attack might just prove to be a prevalent threat if collusion among these institutions takes place. This paper proposes a methodology where we can use intelligent software agents to monitor the activity of stakeholders in the blockchain networks to detect anomaly such as collusion, using supervised machine learning algorithm and algorithmic game theory and stop the majority-attack from taking place.