Bitcoin Illegal Transaction Detection Model Based on Time Step and Ensemble Learning

In recent years, bitcoin, as the leading digital currency, has grown in value. However, due to the anonymity of the bitcoin system, it is convenient for people to carry out illegal activities on it. This will lead to irreversible loss to rights and interests of investors, as well as the proliferation of criminal activities, resulting in significant economic losses. Thus, detecting and combating illegal transactions becomes crucial. This research is dedicated to solving the problem of detecting illegal transactions in open-source bitcoin transaction datasets. We proposes a bitcoin illegal transaction detection model based on time step and ensemble learning. The model focuses on the time-sensitive nature of illegal behaviour in reality by grouping the dataset at the time step. Moreover, the model leverages oversampling techniques in the ensemble learning stage to improve the recall. Experimental results indicate that the proposed model makes it easier for the classifiers at each time step to capture the prevalent illegal patterns of the bitcoin system on different time steps. Results also show that this model can achieve high precision and recall precision=0.99, recall=0.9 in the scope of elliptic dataset, thus improving the detection rate of illegal transactions.