Track and Trace: Automatically Uncovering Cross-chain Transactions in the Multi-blockchain Ecosystems

2025-04-02

AI Summary

This work addresses the challenges of cross-chain transaction traceability and opaque malicious fund flows in decentralized finance (DeFi) multi-chain ecosystems. We propose ABCTRACER, the first bidirectional adaptive cross-chain tracing framework tailored for DeFi. ABCTRACER synergistically integrates explicit event log mining with implicit semantic retrieval, leveraging transaction log parsing, named entity recognition (NER), information retrieval-based encoding, and graph-based association modeling to enable unsupervised discovery of latent asset-flow correlations across heterogeneous blockchains. Evaluated on 12 major cross-chain bridges, ABCTRACER achieves a high bidirectional tracing accuracy of 91.75% F1-score. It has been successfully deployed in real-world investigations of cross-chain exploits and money laundering incidents, substantially enhancing cross-chain transaction traceability and on-chain financial security.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

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 DeFispecific considerations. This paper proposes ABCTRACER, 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, ABCTRACER automatically extracts explicit cross-chain cues. These cues are then combined with information retrieval techniques to encode implicit cues. ABCTRACER facilitates the autonomous learning of latent associated information and achieves bidirectional, generalized cross-chain transaction tracing. Our experiments on 12 mainstream cross-chain bridges demonstrate that ABCTRACER attains 91.75% bi-directional traceability (F1 metrics) with self-adaptive capability. Furthermore, we apply ABCTRACER to real-world cross-chain attack transactions and money laundering traceability, thereby bolstering the traceability and blockchain ecological security of DeFi bridging applications.

Problem

• Automating detection of cross-chain transactions in DeFi ecosystems
• Addressing security gaps in tracing cross-chain asset flows
• Enhancing traceability for decentralized finance bridging applications

Innovation

• Automated bi-directional cross-chain transaction tracing
• Uses event log mining and entity recognition
• Self-adaptive with 91.75% traceability accuracy