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In this paper, we address the critical challenges of double-spending andselfish mining attacks in blockchain-based digital currencies. Double-spendingis a problem where the same tender is spent multiple times during a digitalcurrency transaction, while selfish mining is an intentional alteration of ablockchain to increase rewards to one miner or a group of miners. We introducea new attack that combines both these attacks and propose a machinelearning-based solution to mitigate the risks associated with them.Specifically, we use the learning automaton, a powerful online learning method,to develop two models, namely the SDTLA and WVBM, which can effectively defendagainst selfish mining attacks. Our experimental results show that the SDTLAmethod increases the profitability threshold of selfish mining up to 47$\%$,while the WVBM method performs even better and is very close to the idealsituation where each miner's revenue is proportional to their shared hashprocessing power. Additionally, we demonstrate that both methods caneffectively reduce the risks of double-spending by tuning the $Z$ Parameter.Our findings highlight the potential of SDTLA and WVBM as promising solutionsfor enhancing the security and efficiency of blockchain networks.

New intelligent defense systems to reduce the risks of Selfish Mining and Double-Spending attacks using Learning Automata