A proof of work: securing majority-attack in blockchain using machine learning and algorithmic game theory
Автор: Somdip Dey
Журнал: International Journal of Wireless and Microwave Technologies @ijwmt
Статья в выпуске: 5 Vol.8, 2018 года.
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Blockchain’s vast applications in different industries have drawn several researchers to pursue extensive research in securing blockchain technologies. In recent times 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.
Computer Security, network, blockchain, machine learning, algorithmic game theory, majority attack, anomaly detection
Короткий адрес: https://sciup.org/15016941
IDR: 15016941 | DOI: 10.5815/ijwmt.2018.05.01
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