Anomaly detection in bitcoin information networks with multi-constrained meta path

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.