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

• URL: http://arxiv.org/abs/2504.01822v1
• Date: Wed, 02 Apr 2025 15:28:25 GMT
• Title: Track and Trace: Automatically Uncovering Cross-chain Transactions in the Multi-blockchain Ecosystems
• Authors: Dan Lin, Ziye Zheng, Jiajing Wu, Jingjing Yang, Kaixin Lin, Huan Xiao, Bowen Song, Zibin Zheng,
• Abstract summary: Cross-chain technology enables seamless asset transfer and message-passing within decentralized finance (DeFi) ecosystems.<n>This paper proposes ABCTRACER, an automated, bi-directional cross-chain transaction tracing tool, specifically designed for DeFi ecosystems.
• Score: 21.470299762211546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Trusted Compute Units: A Framework for Chained Verifiable Computations [41.94295877935867]
  This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable computations across heterogeneous technologies. By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability.
  arXiv  Detail & Related papers  (2025-04-22T09:01:55Z)
• Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks [2.855856661274715]
  Existing methods primarily focus on snapshots of transaction networks. We propose an incremental approach for random walk-based node representation learning in transaction networks. Potential applications include transaction network monitoring, the efficient classification of blockchain addresses for fraud detection or the identification of specialized address types within the network.
  arXiv  Detail & Related papers  (2025-01-21T20:34:38Z)
• AsyncSC: An Asynchronous Sidechain for Multi-Domain Data Exchange in Internet of Things [38.94331838546716]
  Current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks.<n>In this paper, we propose AsyncSC, a novel asynchronous sidechain construction.<n>Results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead.
  arXiv  Detail & Related papers  (2024-12-17T09:43:37Z)
• MAP the Blockchain World: A Trustless and Scalable Blockchain Interoperability Protocol for Cross-chain Applications [18.33587076597966]
  interoperability protocols enable cross-chain asset transfers or data retrievals between isolated chains. Existing protocols either face severe scalability issues due to high on-chain and off-chain costs, or suffer from trust concerns because of centralized designs. We propose textttMAP, a trustless blockchain interoperability protocol that relays cross-chain transactions across heterogeneous chains with high scalability.
  arXiv  Detail & Related papers  (2024-11-01T07:43:11Z)
• CONNECTOR: Enhancing the Traceability of Decentralized Bridge Applications via Automatic Cross-chain Transaction Association [23.89344998976908]
  Cross-chain transaction association identifies and matches unique transactions executed by bridge DApps.<n>We present CONNECTOR, an automated cross-chain transaction association analysis method based on bridge smart contracts.<n>We conduct real-world experiments on different types of bridges to demonstrate the effectiveness of CONNECTOR.
  arXiv  Detail & Related papers  (2024-09-08T00:52:06Z)
• SPOQchain: Platform for Secure, Scalable, and Privacy-Preserving Supply Chain Tracing and Counterfeit Protection [46.68279506084277]
  This work proposes SPOQchain, a novel blockchain-based platform that provides comprehensive traceability and originality verification. It provides an analysis of privacy and security aspects, demonstrating the need and qualification of SPOQchain for the future of supply chain tracing.
  arXiv  Detail & Related papers  (2024-08-30T07:15:43Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)
• SoK: Security of Cross-chain Bridges: Attack Surfaces, Defenses, and Open Problems [43.80265187232706]
  Cross-chain bridges are used to facilitate token and data exchanges across blockchains. Although bridges are becoming increasingly popular, they are still in their infancy and have been attacked multiple times recently. This paper analyzes the security landscape of cross-chain bridges in a holistic manner.
  arXiv  Detail & Related papers  (2023-12-19T20:13:21Z)
• A New Paradigm in Blockchain-based Financial Aid Distribution [0.7366405857677227]
  Technology has emerged as a game-changer in a variety of industries, providing robust solutions that can conventional procedures. The financial sector is making substantial progress toward implementing blockchain solutions for a variety of operations. The healthcare industry is simultaneously incorporating this technology into systems for managing medical records, tracing supply chains, and data management. Diverse industries, including real estate, energy, and government, are actively investigating the potential of blockchain to improve efficiency, security, and transparency.
  arXiv  Detail & Related papers  (2023-11-14T19:41:24Z)
• Blockchain Large Language Models [65.7726590159576]
  This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
  arXiv  Detail & Related papers  (2023-04-25T11:56:18Z)
• Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking [5.579169055801065]
  We study the problem of transaction tracking via link prediction, which provides a deeper understanding of transactions from a network perspective. Specifically, we introduce an embedding based link prediction framework that is composed of temporal-amount snapshot multigraph (TASMG) and present temporal-amount walk (TAW) By taking the realistic rules and features of transaction networks into consideration, we propose TASMG to model transaction records as a temporal-amount network and then present TAW to effectively embed accounts via their transaction records.
  arXiv  Detail & Related papers  (2021-02-16T08:21:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.