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:Bitcoin加密货币最近受到了很多关注。 在Bitcoin网络中, 交易记录在分类账中。 在这个网络中, 记录交易的过程取决于一些称为矿工的节点, 执行一个称为采矿协议的协议。 采矿协议的一个重要方面是激励兼容性。 但是, 文献表明, Bitcoin采矿协议不具有激励兼容性。 一些计算能力高的节点可以通过采用一种称为自私的采矿攻击来获得比公平份额更多的收入。 在本文中, 我们建议采用学习自动化的理论, 进行基于情报的人工防御, 防止自私的采矿攻击。 拟议的防御机制忽视私人区块, 依据街区发现时间来分配重量, 并改变目前Bitcoin的堡垒, 利用学习自动化数据来评估分支的高度差异。 根据我们的知识, 拟议的协议是文献的第一个基于学习的防御机制。 模拟结果显示, 防止断线机制的优越性, 这是一种众所周知的防御。 模拟结果显示, 以区隔机制的优势在于, 自私的门槛意味着, 增长 和 。