基于人工免疫的比特币快捷交易异常检测模型

电子货币交易最重要的问题是双重花费（双花攻击），比特币预防双花攻击的策略是等待六个确认块（约1 h），难以适用于快捷支付领域，默认替代策略是等待交易信息传播到卖主的钱包，这无法有效地预防双花攻击。针对比特币快捷交易中双花攻击的检测问题，提出了一种基于人工免疫的比特币快捷交易异常检测模型。在每个传统比特币节点中加入免疫检测模块进行抗原提取，并利用检测器进行异常检测，在威胁控制中心动态演化检测器并分发免疫疫苗以便有效地进行防御。实验结果证明，此检测模型能够有效地检测并预防比特币快捷支付中的双花攻击。

Artificial immune based bitcoin fast transaction anomaly detection model

The most important issue in e-currency trading is double spending attack. The strategy of Bitcoin to prevent double spending attack is to wait for 6 confirmation blocks(about 1 h), which is difficult to apply to the fast payment field. The default alternative strategy is to wait for the transaction information to propagate to the seller''s wallet, which is not effective against double spending attacks. In order to solve the detection of double spending attacks in Bitcoin fast transactions, this paper proposed an artificial immune based Bitcoin fast transaction anomaly detection model. Each conventional Bitcoin node added an immune module for antigen extraction and abnormal detection, and the threat control center evolved the detector dynamically and distributed immune vaccines to effectively perform defense. The experimental results show that this detection model can effectively detect and prevent double spending attacks in Bitcoin fast payment.