An Extensive Survey on the Application of Machine Learning in CEAT: Categorizing Ethereum Addresses Transaction Analysis
Keywords:
Blockchain, CEAT, Ethereum, Ethereum virtual machine, Machine learning, Smart contractsAbstract
The capacity to handle smart contracts and decentralized apps has led to Ethereum's (a decentralized blockchain platform) enormous acceptance. However, the exponential growth of Ethereum’s transaction data presents significant challenges in efficiently categorizing Ethereum addresses for various purposes, including fraud detection, transaction analysis, and user behavior profiling. This paper presents a comprehensive survey of Machine Learning (ML) techniques applied to the classification of Ethereum Addresses' Transactions (CEAT). They explore the application of supervised, unsupervised, and semi-supervised learning methods in analyzing Ethereum transaction patterns, identifying anomalies, and enhancing blockchain security. Additionally, the paper discusses how ML can contribute to the scalability, transparency, and robustness of Ethereum’s ecosystem, particularly in real-time transaction analysis and decision-making. By reviewing recent research, they highlight the potential of ML models to improve the efficiency and accuracy of Ethereum address classification. The challenges posed by the high-dimensional nature of blockchain data, evolving transaction patterns, and privacy concerns are also examined, along with the strategies proposed in the literature to address these obstacles. Finally, the paper identifies promising future directions for the integration of advanced ML techniques to enhance the classification of Ethereum addresses and improve the overall security and usability of the Ethereum blockchain.
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