Securing Majority-Attack In Blockchain Using Machine Learning And Algorithmic Game Theory: A Proof of Work
It addresses security for consortium blockchain networks where institutions collaborate, but the approach appears incremental.
The paper tackles the threat of majority-attack in consortium-based blockchain networks by proposing a methodology using intelligent software agents, supervised machine learning, and algorithmic game theory to detect collusion and prevent attacks.
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.