Dan Lin, Ziye Zheng, Jiajing Wu, Jingjing Yang, Kaixin Lin, Huan Xiao, Bowen Song, Zibin Zheng
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.