Abstract
Forensic investigations increasingly leverage artificial intelligence (AI/ML) to identify illegal activities on bitcoin. bitcoin transactions have an original graph (network) structure, which is sophisticated and yet informative. However, machine learning applications on bitcoin have given limited attention to developing end-to-end deep learning frameworks that are modeled to exploit the bitcoin graph structure. To identify illegal transactions on bitcoin, the current paper extracts nineteen features from the bitcoin network and proposes a deep learning-based graph neural network model using spectral graph convolutions and transaction features. The proposed model is compared with two state-of-the-art techniques, viz., a graph attention network (GAT2) and an extreme gradient boosted decision tree (XGBOOST) trained on convoluted features for classification of illegal transactions on bitcoin. To understand the efficacy of the proposed model, a dataset is collected consisting of 13310125 transactions of 2059 entities having 3152202 bitcoin account addresses and belonging to 28 categories of users. Two sets of experiments are performed on the datasets: labeling transactions as legal or illegal (binary classification) and identifying the originator of the transaction to one of the twenty-eight types of entities (multi-class classification). For fast and accurate decisions, binary classification is appropriate, and for pinpointing the category of bitcoin users, a multi-class classifier is suitable. On both the tasks, the proposed models achieved a maximum of 92% accuracy, validating the methodology and suitability of the model for real-world deployment.
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Enquiries about data availability should be directed to the authors.
Notes
Bitcoin refers to the system, and bitcoin or BTC refers to the digital currency.
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Funding
This work was supported in part by the Raman Charpak Fellowship of the Indo-French Centre for the Promotion of Advanced Research Grant no: IFC/4132/RCF 2019/716. The authors thank Prof. Dhiren Patel and Prof. Sunil Bhirud VJTI Mumbai, NMIMS University Mumbai and Prof. Yann Busnel and Prof. Romaric Ludinard IMT Atlantique, France for providing the lab resources.
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Nerurkar, P. Illegal activity detection on bitcoin transaction using deep learning. Soft Comput 27, 5503–5520 (2023). https://doi.org/10.1007/s00500-022-07779-1
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DOI: https://doi.org/10.1007/s00500-022-07779-1