  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%.
