Mitigating Blockchain extractable value (BEV) threats by Distributed Transaction Sequencing in Blockchains

TL;DR

This work addresses Transaction Ordering Dependence (TOD), Blockchain Extractable Value (BEV), and Transaction Importance Diversity (TID) in DeFi by proposing the Distributed Transaction Sequencing Strategy (DTSS), which leverages forking and Analytic Hierarchy Process (AHP) to enforce fair and transparent transaction ordering. It introduces a Normalized Allocation Disparity Metric (NADM) to optimize DTSS parameters and demonstrates, through simulations and BEV attack scenarios, that DTSS can significantly reduce adversarial transaction exploitation while improving fairness for time-sensitive operations. Key contributions include a formal relationship between forking and block size, a practical DTSS architecture with mining/propagation stages, an AHP-based weighting scheme, an explicit optimization framework, and extensive experimental validation showing improved security and transparency in DeFi ecosystems. The approach holds practical significance for integrating DeFi with traditional finance and for securing future blockchain systems, with potential extensions to permissionless settings and broader BEV-attack classes.

Abstract

The rapid growth of Blockchain and Decentralized Finance (DeFi) has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem. This study identifies critical issues such as Transaction Order Dependence (TOD), Blockchain Extractable Value (BEV), and Transaction Importance Diversity (TID), which collectively undermine the fairness and security of DeFi systems. BEV-related activities, including Sandwich attacks, Liquidations, and Transaction Replay, have emerged as significant threats, collectively generating $540.54 million in losses over 32 months across 11,289 addresses, involving 49,691 cryptocurrencies and 60,830 on-chain markets. These attacks exploit transaction mechanics to manipulate asset prices and extract value at the expense of other participants, with Sandwich attacks being particularly impactful. Additionally, the growing adoption of Blockchain in traditional finance highlights the challenge of TID, where high transaction volumes can strain systems and compromise time-sensitive operations. To address these pressing issues, we propose a novel Distributed Transaction Sequencing Strategy (DTSS), which combines forking mechanisms and the Analytic Hierarchy Process (AHP) to enforce fair and transparent transaction ordering in a decentralized manner. Our approach is further enhanced by an optimization framework and the introduction of the Normalized Allocation Disparity Metric (NADM), which ensures optimal parameter selection for transaction prioritization. Experimental evaluations demonstrate that DTSS effectively mitigates BEV risks, enhances transaction fairness, and significantly improves the security and transparency of DeFi ecosystems. This work is essential for protecting the future of decentralized finance and promoting its integration into global financial systems.

Figures (13)

• Figure 1: Illustration of Sandwich attacks and two scenarios: "buy" and "sell". In scenario (Buy), predatory trader buys the same asset prior to $T_{V}$ affecting the market price, enabling predatory trader to sell the asset at a higher price following the transaction. In scenario (Sell), predatory trader sells the same asset before $T_{V}$ impacts the market price, allowing predatory trader to repurchase the asset at a lower price after the transaction.
• Figure 2: Blockchain ecosystem experiences a range of issues related to BEV and TID challenges, including predatory traders' Sandwich and Replay Attacks (Block A'), exploitation of liquidation or arbitrage opportunities (Block C'), and efficiency hindrance from non-time-sensitive transactions (Block D').
• Figure 4: Illustration of how Distributed Transaction Sequencing Strategy (DTSS) works in the propagation stages
• Figure 5: DTSS
• Figure 6: Illustration of how to insert adversary transaction to simulate Sandwich attack, replay attack, and clogging