Modeling and Simulation of Selfish Mining Attacks in Blockchain Network using Evolutionary Game Theory

This paper presents a method of attacking proof of work consensus based on selfish mining. The current mitigation strategies for the blockchain network's egotistical mining are not self-sufficient after a certain number of generations. Additionally, these solutions do not address the network nodes' cooperative and defector behavior. Additionally, more blocks from self-centered nodes are added to the blockchain in this development. This study analyzes to what extent these risks may affect cryptocurrency extraction. Minority mining pools keep some blocks private by deviating from the original mining protocol. An attacking collection aims to increase revenue by wasting other miners' computing power. By adopting a novel approach in this study. To determine whether such attacks are profitable. Using the interaction between pools in this model, mining strategies can be derived using game theory. By analyzing the relative revenue rather than the monetary award, this model simulates the game for a Bitcoin blockchain. This illustrates the usefulness of considering the cost of a strategy when discussing the potential outcomes of selfish mining strategies. The author highlights scenarios where the system might be compromised based on the way the parameters are set up in the game.