FRAD: Front-Running Attacks Detection on Ethereum using Ternary Classification Model
This addresses transaction security for Ethereum developers and users, offering a detection method for decentralized applications, but it is incremental as it builds on existing classification approaches.
The paper tackles the problem of detecting front-running attacks on Ethereum by introducing FRAD, a ternary classification model that achieves 84.59% accuracy and 84.60% F1-score in identifying transaction displacement, insertion, and suppression attacks.
With the evolution of blockchain technology, the issue of transaction security, particularly on platforms like Ethereum, has become increasingly critical. Front-running attacks, a unique form of security threat, pose significant challenges to the integrity of blockchain transactions. In these attack scenarios, malicious actors monitor other users' transaction activities, then strategically submit their own transactions with higher fees. This ensures their transactions are executed before the monitored transactions are included in the block. The primary objective of this paper is to delve into a comprehensive classification of transactions associated with front-running attacks, which aims to equip developers with specific strategies to counter each type of attack. To achieve this, we introduce a novel detection method named FRAD (Front-Running Attacks Detection on Ethereum using Ternary Classification Model). This method is specifically tailored for transactions within decentralized applications (DApps) on Ethereum, enabling accurate classification of front-running attacks involving transaction displacement, insertion, and suppression. Our experimental validation reveals that the Multilayer Perceptron (MLP) classifier offers the best performance in detecting front-running attacks, achieving an impressive accuracy rate of 84.59% and F1-score of 84.60%.