  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.
