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 95.95% 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.

Cross-chain technology enables seamless asset transfer and message-passing within decentralized finance (DeFi) ecosystems, facilitating multi-chain coexistence in the current blockchain environment. However, this development also raises security concerns, as malicious actors exploit cross-chain asset flows to conceal the provenance and destination of assets, thereby facilitating illegal activities such as money laundering. Consequently, the need for cross-chain transaction traceability has become increasingly urgent. Prior research on transaction traceability has predominantly focused on single-chain and centralized finance (CeFi) cross-chain scenarios, overlooking DeFi-specific considerations. This paper proposesABCTracer, an automated, bi-directional cross-chain transaction tracing tool, specifically designed for DeFi ecosystems. By harnessing transaction event log mining and named entity recognition techniques,ABCTracerautomatically extracts explicit cross-chain cues. These cues are then combined with information retrieval techniques to encode implicit cues.ABCTracerfacilitates the autonomous learning of latent associated information and achieves bidirectional, generalized cross-chain transaction tracing. Our experiments on 12 mainstream cross-chain bridges demonstrate thatABCTracerattains 91.75% bi-directional traceability (F1 metrics) with self-adaptive capability. Furthermore, we applyABCTracerto real-world cross-chain attack transactions and money laundering traceability, thereby bolstering the traceability and blockchain ecological security of DeFi bridging applications.

As the number of blockchain platforms continues to grow, the independence of these networks poses challenges for transferring assets and information across chains. Cross-chain bridge technology has emerged to address this issue, establishing communication protocols to facilitate cross-chain interaction of assets and information, thereby enhancing user experience. However, the complexity of cross-chain transactions increases the difficulty of security regulation, rendering traditional single-chain detection methods inadequate for cross-chain scenarios. Therefore, understanding cross-chain transaction semantics is crucial, as it forms the foundation for cross-chain security detection tasks. Although there are existing methods for extracting transaction semantics specifically for single chains, these approaches often overlook the unique characteristics of cross-chain scenarios, limiting their applicability. This paper introduces XSema, a novel cross-chain semantic extraction framework grounded in asset transfer and message-passing, designed specifically for cross-chain contexts. Experimental results demonstrate that XSema effectively distinguishes between cross-chain and non-cross-chain transactions, surpassing existing methods by over 9% for the generality metric and over 10% for the generalization metric. Furthermore, we analyze the underlying asset transfer patterns and message-passing event logs associated with cross-chain transactions. We offer new insights into the coexistence of multiple blockchains and the cross-chain ecosystem.

Cross-chain bridges are currently the most popular solution to support asset interoperability between heterogeneous blockchains. Over the past year, there have been more than ten serious attacks against cross-chain bridges, resulting in billions of dollars in losses. Among these attacks, fake deposits stand out as particularly destructive. Hackers can perpetrate such attacks by verifying the authenticity of proof associated with fake deposits on the target blockchain, subsequently pilfering the assets. However, existing tools have limitations in detecting this type of attack. To address this problem, this work proposes a tool to protect cross-chain bridges from fake deposit attacks by analyzing the network of transaction traces. Specifically, the framework first records the execution traces for each transaction, and then extracts the relevant contract interactions therein to extract statistical and structural features. Finally, real case labels are utilized to identify attacking and non-attacking transactions. We conducted experiments to validate the tool’s effectiveness and efficiency. In particular, for the detection of fake deposit transactions, our method achieved an average precision of 0.89, a recall value of 0.83, and the ability to identify 38.65 transactions per second.