UniDetect: LLM-Driven Universal Fraud Detection across Heterogeneous Blockchains

• URL: http://arxiv.org/abs/2604.12329v1
• Date: Tue, 14 Apr 2026 06:04:37 GMT
• Title: UniDetect: LLM-Driven Universal Fraud Detection across Heterogeneous Blockchains
• Abstract summary: We introduce UniDetect, a multi-chain cryptocurrency fraud account detection method based on large language models (LLMs)<n>Specifically, we use domain knowledge to guide the LLM to generate general transaction summary texts applicable to heterogeneous blockchain accounts.<n> Experiments on multiple blockchains show that UniDetect outperforms existing methods 5.57% to 7.58% in Kolmogorov-Smirnov (KS)
• Score: 9.89047849878308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As cross-chain interoperability advances, decentralized finance (DeFi) protocols enable illicit funds to be reorganized into uniform liquid assets that flow throughout the cryptocurrency market. Such operations can bypass monitoring targeted at individual blockchains and thereby weaken current regulatory frameworks. Motivated by these, we introduce UniDetect, a multi-chain cryptocurrency fraud account detection method based on large language models (LLMs). Specifically, we use domain knowledge to guide the LLM to generate general transaction summary texts applicable to heterogeneous blockchain accounts, which serve as evidence for fraud account detection. Furthermore, we introduce a two-stage alternating training strategy to continuously and dynamically enhance the multimodal joint reasoning for detecting fraudulent accounts based on both the textual evidence and the transaction graph patterns. Experiments on multiple blockchains show that UniDetect outperforms existing methods 5.57% to 7.58% in Kolmogorov-Smirnov (KS). For cross-chain zero-shot detection, UniDetect identifies over 94.58% of fraudulent accounts. It also generalizes well to non-blockchain data, delivering a 6.06% improvement in F1 over existing methods. The dataset and source code are available at https://github.com/msy0513/UniDetect.

Related papers

• SoK: Cross-Chain Transaction Identification and Matching [14.090582147532361]
  Cross-chain bridges, instant cryptocurrency exchanges, and centralized cross-ledger platforms move assets across an increasingly multi-chain ecosystem.<n>These systems have repeatedly become targets of high-value attacks and channels for cross-chain money laundering.<n>We present a systematization of knowledge (SoK) on cross-chain transaction identification and matching.
  arXiv  Detail & Related papers  (2026-08-18T08:54:15Z)
• FlowShield: cryptocurrency anti-money laundering with transaction semantics parsing and fund flow tracking [46.45233269509415]
  textscFlowShield is a Crypto AML framework for transaction-level laundering detection and investigator-facing report generation.<n>textscFlowShield reconstructs fund-flow subgraphs from three complementary perspectives.<n>It then employs a text--structure fusion mechanism, enabling the interplay between large language model (LLM)-encoded semantics and flow texts with graph convolutional network (GCN)-encoded structure.
  arXiv  Detail & Related papers  (2026-08-18T04:21:19Z)
• Proof of Source of Funds: Efficient On-chain Provenance of Cryptoassets [5.222265639534245]
  We introduce Proof of Source of Funds (PoSoF), a novel cryptographic framework that shifts the burden to the user.<n>PoSoF provides verifiable cryptographic provenance; it guarantees the legitimacy of the funds without leaking the intermediate transaction topology.<n>Our benchmarks demonstrate that fully private, proactive compliance is highly practical.
  arXiv  Detail & Related papers  (2026-06-08T21:07:02Z)
• Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection [55.18577006122169]
  We propose a novel framework termed textbfTEmporal textbfMotif-aware textbfGraph textbfTest-textbfTime textbfAdaptation (textbfTEMG-TTA)<n>First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism.<n>Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs.
  arXiv  Detail & Related papers  (2026-05-28T07:43:20Z)
• A Survey of Transaction Tracing Techniques for Blockchain Systems [2.307833568494795]
  We perform a systematic review of the various tracing techniques for blockchain transactions proposed in literature.<n>We provide insights on the state of blockchain transaction tracing literature and identify the limitations of existing approaches.
  arXiv  Detail & Related papers  (2025-09-23T05:21:20Z)
• Efficient Blockchain-based Steganography via Backcalculating Generative Adversarial Network [105.47203971578871]
  We propose a generic blockchain-based steganography framework (GBSF)<n>The sender generates the required fields such as amount and fees, where the additional covert data is embedded to enhance the channel capacity.<n>Based on GBSF, we design a reversible generative adversarial network (R-GAN)<n>We propose R-GAN with Counter-intuitive data preprocessing and Custom activation functions, namely CCR-GAN.
  arXiv  Detail & Related papers  (2025-06-19T04:43:41Z)
• Track and Trace: Automatically Uncovering Cross-chain Transactions in the Multi-blockchain Ecosystems [21.470299762211546]
  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.
  arXiv  Detail & Related papers  (2025-04-02T15:28:25Z)
• BlockScan: Detecting Anomalies in Blockchain Transactions [16.73896087813861]
  BlockScan is a customized Transformer for anomaly detection in blockchain transactions.<n>This work sets a new benchmark for applying Transformer-based approaches in blockchain data analysis.
  arXiv  Detail & Related papers  (2024-10-05T05:11:34Z)
• 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)
• Leveraging Machine Learning for Multichain DeFi Fraud Detection [5.213509776274283]
  We present a framework for extracting features from different chains, including the largest one, and it is evaluated over an extensive dataset. Different Machine Learning methods were employed, such as XGBoost and a Neural Network for identifying fraud accounts detection interacting with DeFi. We demonstrate that the introduction of novel DeFi-related features, significantly improves the evaluation results.
  arXiv  Detail & Related papers  (2023-05-17T15:48:21Z)
• 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)
• Reputation-based PoS for the Restriction of Illicit Activities on Blockchain: Algorand Usecase [2.3455387491754975]
  In recent times, different machine learning-based techniques can detect such criminal elements based on blockchain transaction data.<n>We propose a reputation-based methodology for response to the users detected carrying out the aforementioned illicit activities.
  arXiv  Detail & Related papers  (2021-12-21T07:32:22Z)

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.