As the most popular digital currency, Bitcoin has a high economic value, and its security has been paid more and more attention. Anomaly detection of Bitcoin has become a problem that must be solved. The existing Bitcoin anomaly detection methods only use static network models, and only the simple structural features such as node attributes and in/out-degree are considered to measure the similarities between nodes. Therefore, we propose a series of constrained anomaly detection algorithms for Bitcoin data. In our algorithms, we first construct a temporal Bitcoin network model for Bitcoin data. Then, combining time constraints, attribute constraints and structure constraints, a multi-constrained meta path is proposed on the basis of the meta path to specify the candidate sets, reference sets and similarity measurement strategies and detect local abnormal users and transactions that are of interest to users from static and dynamic angles with lower space-time overhead. Experiments on real-world Bitcoin data show that the constrained algorithms have certain improvements in recall, precision and F2 score when compared to the algorithms that only considers simple structural features such as node attributes and in/out-degree.

作为最受欢迎的数字货币，比特币具有很高的经济价值，其安全性越来越受到关注。比特币的异常检测已经成为必须解决的问题。现有的比特币异常检测方法仅使用静态网络模型，并且仅考虑诸如节点属性和进/出度之类的简单结构特征来测量节点之间的相似性。因此，我们提出了一系列针对比特币数据的约束异常检测算法。在我们的算法中，我们首先为比特币数据构建时间比特币网络模型。然后，结合时间约束，属性约束和结构约束，在元路径的基础上提出多约束元路径来指定候选集，参考集和相似性度量策略，并以较低的时空开销从静态和动态角度检测用户感兴趣的本地异常用户和交易。对现实世界比特币数据的实验表明，与仅考虑简单结构特征（例如节点属性和进/出度）的算法相比，受约束的算法在召回率，精度和F2得分方面有一定的提高。