CONNECTOR: Enhancing the Traceability of Decentralized Bridge Applications via Automatic Cross-chain Transaction Association

TL;DR

CONNECTOR tackles the challenge of cross-chain traceability for contract-based DeFi bridges by introducing an automated, API-free framework that first identifies deposit transactions from bridge contracts using both functional input data and token-aware call graphs, then matches corresponding withdrawals through syntactic-semantic log parsing and bridge-specific business logic. The approach yields near-perfect deposit identification ($\approx$100\%) and high withdrawal matching rates (often $>$92\%, with peak $\approx$99\% for some bridges) across a diverse set of bridges, outperforming CeFi-based methods and bridge explorers in many scenarios. The work provides a large, open dataset of $24{,}392$ cross-chain transaction pairs, insights into user behavior, timing, and fees, and demonstrates potential AML applications, demonstrating practical impact for developers, regulators, and users in the multi-chain DeFi landscape.

Abstract

Decentralized bridge applications are important software that connects various blockchains and facilitates cross-chain asset transfer in the decentralized finance (DeFi) ecosystem which currently operates in a multi-chain environment. Cross-chain transaction association identifies and matches unique transactions executed by bridge DApps, which is important research to enhance the traceability of cross-chain bridge DApps. However, existing methods rely entirely on unobservable internal ledgers or APIs, violating the open and decentralized properties of blockchain. In this paper, we analyze the challenges of this issue and then present CONNECTOR, an automated cross-chain transaction association analysis method based on bridge smart contracts. Specifically, CONNECTOR first identifies deposit transactions by extracting distinctive and generic features from the transaction traces of bridge contracts. With the accurate deposit transactions, CONNECTOR mines the execution logs of bridge contracts to achieve withdrawal transaction matching. We conduct real-world experiments on different types of bridges to demonstrate the effectiveness of CONNECTOR. The experiment demonstrates that CONNECTOR successfully identifies 100% deposit transactions, associates 95.81% withdrawal transactions, and surpasses methods for CeFi bridges. Based on the association results, we obtain interesting findings about cross-chain transaction behaviors in DeFi bridges and analyze the tracing abilities of CONNECTOR to assist the DeFi bridge apps.

Figures (11)

• Figure 1: A user making a cross-chain transaction from Ethereum to BNB chain includes three main steps: ① User locks assets to bridge contract on Ethereum (deposit transaction $tx_{dep}$); ② The bridge verifies the transaction; ③ The bridge contract unlocks the corresponding value of the asset to the user on the BNB chain (withdrawal transaction $tx_{wth}$).
• Figure 2: CeFi bridges vs DeFi bridges.
• Figure 3: Overview of Connector.
• Figure 4: Example deposit functions defined in Poly Network, Celer cBridge, and Multichain.
• Figure 5: The simplified implementation of triggered contracts and functions corresponding to the example transaction 0xa7ef.