FRACE: Front-Running Attack Classification on Ethereum using Ensemble Learning

Abstract

Abstract With the rapid evolution of blockchain technologies, Ethereum has emerged as a central platform for advanced financial applications but has concurrently experienced a rise in security vulnerabilities, particularly from front-running attacks. These attacks exploit transaction sequencing for illegal gains. To combat this, we introduce FRACE (Front-Running Attack Classification using Ensemble Learning), a novel methodology that classifies front-running attacks into displacement, insertion, and suppression using an ensemble learning model. This precise classification facilitates tailored defensive strategies, enhancing the robustness and accuracy of attack detection. Our approach achieves an accuracy of 95.36% and an F1-score of 95.30%, significantly improving the security of decentralized applications. Extensive analysis and validation on Ethereum confirm these results. Future efforts will refine these models and extend their application to other blockchain platforms, striving for a universally secure, transparent, and reliable digital transaction ecosystem.