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- W4296087369 abstract "Abstract Forensic investigations increasingly leverage artificial intelligence 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 an Ensemble of Decision Trees, and a Decision trees 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." @default.
- W4296087369 created "2022-09-17" @default.
- W4296087369 creator A5091449753 @default.
- W4296087369 date "2022-09-15" @default.
- W4296087369 modified "2023-10-18" @default.
- W4296087369 title "Illegal Activity Detection on Bitcoin Transaction using Deep Learning" @default.
- W4296087369 doi "https://doi.org/10.21203/rs.3.rs-1454891/v1" @default.
- W4296087369 hasPublicationYear "2022" @default.
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