A New Approach for Detecting Selfish-Mining Attacks in Blockchain Networks
International journal of intelligent engineering and systems · 2023
The identification of selfish mining (SM) activity is critical in blockchain mining.In exchange for successfully mining blocks, volunteers known as miners maintain the network (N/W).Assigning transactions to a block requires verifying each one, then adding the block to the blockchain and broadcasting it to the peers.This paper investigates the profitability of Bitcoin block-discarding attacks.To represent the blockchain's dynamics, a discrete event simulator is built.A simple N/W concept is employed by everyone as a miner.It is assumed that all miners are honest and that all block broadcasts are latencies, to begin with.In the case of blockchain, the latency produces splits, which are rapidly resolved.The proposed N/W model's simulation findings closely match the real-world Bitcoin N/W observations.According to the selfish-mine block discarding attack, a small group of cooperating miners can corrupt the Bitcoin system.Both immediate block transmission and latencies block are used to test this claim.This research suggested to use of metaheuristic algorithms such as ant colony optimization (ACO) and particle swarm optimization (PSO) to the mining process and applies them to previous solutions of SM detection to runtime efficiency and solution quality performance and contribute to reducing SM behavior all that by determine the optimal threshold α, optimal hash powering, and the optimal execution time to gain profits with the maximum relative revenue.Finally, our findings indicate that, when optimized, SM performs better than ordinary SM in terms of relative revenue and confirmed blocks.SM is profitable when the threshold is 0.6 and the results appear that the ACOSM optimization is better than PSOSM optimization.