【Author】
Zhang, Rui; Zhang, Guifa; Liu, Lan; Wang, Chen; Wan, Shaohua
【Source】JOURNAL OF SYSTEMS ARCHITECTURE
【Abstract】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 realworld 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.
【Keywords】Anomaly detection; Bitcoin network; Constraint condition; Fusion similarity
【摘要】比特币作为目前最流行的数字货币，具有很高的经济价值，其安全性也越来越受到人们的关注。比特币的异常检测已经成为一个必须解决的问题。现有的比特币异常检测方法仅使用静态网络模型，仅考虑节点属性、进出度等简单的结构特征来衡量节点之间的相似性。因此，我们针对比特币数据提出了一系列约束异常检测算法。在我们的算法中，我们首先为比特币数据构建了一个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，在此基础上提出一个多约束元路径来指定候选集;参考集和相似度度量策略，从静态和动态的角度检测用户感兴趣的局部异常用户和事务，具有较低的空时开销。在现实比特币数据上的实验表明，与只考虑节点属性、入/出度等简单结构特征的算法相比，约束算法在召回率、准确率和F2评分方面都有一定的提高。
【关键词】异常检测;比特币网络;约束条件;融合相似
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