Survey on Ethereum phishing detection technology

Abstract

With the widespread application of blockchain technology, phishing scams have become a major threat to blockchain platforms.Due to the irreversibility, anonymity, and tamper-proof nature of blockchain transactions, phishing attacks often have a high degree of deception and concealment, causing significant losses to both users and businesses.Ethereum platform, with its smart contract functionality, has attracted many crypto currency investors.However, this widespread popularity has also attracted an influx of criminals, leading to the rise of cybercrime activities.Among them, phishing scams are one of the main forms of fraud on the Ethereum platform.To tackle this issue, researchers have developed Ethereum network phishing identification technology, achieving significant progress in this field.However, there has been relatively little systematic analysis and summary of these research results.The current state of phishing fraud on the Ethereum network was analyzed.Moreover, a comprehensive summary of existing phishing detection datasets and evaluation metrics were provided.On this basis, methods for detecting phishing on Ethereum were reviewed, including those based on transaction information, graph embedding and graph neural networks.Transaction information-based methods are the most common, analyzing information such as input and output addresses and amounts in transaction data to determine whether a transaction is abnormal.Methods based on graph embedding and graph neural networks place more emphasis on analyzing the entire transaction network, constructing a graph structure to analyze the relationships between nodes, and more accurately identifying phishing attacks.In addition, a comparative analysis of the advantages and disadvantages of various methods was conducted, explaining the applicability and limitations of each method.Finally, the challenges facing Ethereum phishing detection were pointed out, and the future research trends for Ethereum phishing detection were predicted.

Keywords

• blockchain
• ethereum
• phishing detection
• graph neural network