On-graph Machine Learning-based Fraud Detection in Ethereum Cryptocurrency Transactions.

The popularity of Ethereum as a platform for Stablecoin transactions (for example, AUDN) continues to rise. It is therefore paramount that the integrity and security of transactions within these decentralized systems are guaranteed. The intricate network of interactions occurring during the exchange of cryptocurrencies made the task of identifying specific transactions as fraudulent difficult because fraudulent behaviour can be concealed within legitimate smart contract operations. Leveraging the inherent structure and interconnectedness of Ethereum transactions, this paper proposes a comprehensive framework to address issues such as Frontrunning within the cryptocurrency ecosystem. Constructing a knowledge graph representation of fraudulent Ethereum blockchain transactions, the proposed solution captures the relationships between addresses, transactions, and smart contracts and generates BotVictim recommendations based on Victim Receiver similarity scores exceeding 85%. These results are generated by excluding temporal transactions, a unique approach when examining the Ethereum network. Thus, our approach enables early detection and prevention of fraudulent activities, potentially safeguarding the interests of cryptocurrency users and mitigating potential financial losses. To evaluate the effectiveness of the proposed framework, its performance is compared against traditional fraud detection methods. The proposed solution demonstrates superiority in terms of accuracy and efficiency.