The Bitcoin cryptocurrency has received much attention recently. In the network of Bitcoin, transactions are recorded in a ledger. In this network, the process of recording transactions depends on some nodes called miners that execute a protocol known as mining protocol. One of the significant aspects of mining protocol is incentive compatibility. However, literature has shown that Bitcoin mining's protocol is not incentive-compatible. Some nodes with high computational power can obtain more revenue than their fair share by adopting a type of attack called the selfish mining attack. In this paper, we propose an artificial intelligence-based defense against selfish mining attacks by applying the theory of learning automata. The proposed defense mechanism ignores private blocks by assigning weight based on block discovery time and changes current Bitcoin's fork resolving policy by evaluating branches' height difference in a self-adaptive manner utilizing learning automata. To the best of our knowledge, the proposed protocol is the literature's first learning-based defense mechanism. Simulation results have shown the superiority of the proposed mechanism against tie-breaking mechanism, which is a well-known defense. The simulation results have shown that the suggested defense mechanism increases the profit threshold up to 40\% and decreases the revenue of selfish attackers.

翻译：比特币加密货币近期受到广泛关注。在比特币网络中，交易记录存储在账本中。该网络的交易记录过程依赖于被称为矿工的节点，这些节点执行名为挖矿协议的程序。挖矿协议的重要特性之一是激励相容性。然而研究表明，比特币挖矿协议并非激励相容。部分具有高计算能力的节点可通过采用名为自私挖矿攻击的手段获取超额收益。本文提出一种基于学习自动机理论的人工智能防御机制来对抗自私挖矿攻击。该防御机制通过根据区块发现时间分配权重来忽略私有区块，并利用学习自动机以自适应方式评估分支高度差，从而改变比特币现有分叉解决策略。据我们所知，该协议是文献中首个基于学习的防御机制。仿真结果表明，相较于著名防御机制"平局打破机制"，本机制具有显著优越性。仿真数据显示，建议的防御机制可将收益阈值提升达40%，同时降低自私攻击者的收益。