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%.

翻译：摘要：随着区块链技术的发展，以太坊等平台的交易安全问题日益凸显。抢先交易攻击作为一种特殊的安全威胁形式，对区块链交易的完整性构成了重大挑战。在此类攻击场景中，恶意行为者通过监控其他用户的交易活动，以更高手续费策略性地提交自身交易，确保其交易在监控目标交易被纳入区块前优先执行。本文旨在深入探究与抢先交易攻击相关的交易分类体系，为开发者提供针对各类攻击的特定应对策略。为此，我们提出一种名为FRAD（基于三元分类模型的以太坊抢先交易攻击检测方法）的新型检测方法。该方法专门针对以太坊去中心化应用中的交易场景，能够实现对涉及交易置换、插入和抑制的抢先交易攻击的精准分类。实验验证表明，多层感知器分类器在检测抢先交易攻击时性能最优，准确率达到84.59%，F1分数为84.60%。