A Method for Detecting Illegal Bitcoin Transactions Based on Multi-feature Fusion

As digital currencies, notably, Bitcoin, gain widespread adoption, the detection of illicit transactions poses a pressing challenge that requires prompt resolution. This research introduces a novel approach for detecting illicit Bitcoin transactions that integrates diverse features to enhance both detection efficiency and accuracy. Firstly, a comprehensive feature set is constructed by amalgamating conventional data features with those uniquely derived from LSTM, RandomWalk, and PageRank algorithms, enabling the capture of intricate patterns within transaction data. Secondly, to address the class imbalance inherent in Bitcoin transaction datasets, FocalLoss is adopted as the loss function, strengthening the model’s ability to discern minority classes (i.e., illicit transactions). Finally, the model is validated on the Elliptic dataset using a multilayer perceptron (MLP) architecture with a single hidden layer, and its performance is compared with current mainstream Bitcoin illegal transaction detection models (GAT, GCN). Experimental results demonstrate that the proposed method achieves significant improvements in crucial metrics such as F1 score and recall rate compared to traditional methods, validating the effectiveness of the multi-feature fusion strategy and the utilization of FocalLoss in tackling the challenge of illegal Bitcoin transaction detection.