Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking

• URL: http://arxiv.org/abs/2102.08013v1
• Date: Tue, 16 Feb 2021 08:21:16 GMT
• Title: Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking
• Authors: Yunyi Xie, Jie Jin, Jian Zhang, Shanqing Yu, and Qi Xuan
• Abstract summary: 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.
• Score: 5.579169055801065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the wide application of blockchain in the financial field, the rise of various types of cybercrimes has brought great challenges to the security of blockchain. In order to better understand this emerging market and explore more efficient countermeasures for effective supervision, it is imperative to track transactions on blockchain-based systems. Due to the openness of Ethereum, we can easily access the publicly available transaction records, model them as a complex network, and further study the problem of transaction tracking via link prediction, which provides a deeper understanding of Ethereum 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 Ethereum transaction records as a temporal-amount network and then present TAW to effectively embed accounts via their transaction records, which integrates temporal and amount information of the proposed network. Experimental results demonstrate the superiority of the proposed framework in learning more informative representations and could be an effective method for transaction tracking.

Related papers

• Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning [6.378807038086552]
  Current fraud detection methods fail to consider the semantic information and similarity patterns within transactions. We propose TLMG4Eth that combines a transaction language model with graph-based methods to capture semantic, similarity, and structural features of transaction data.
  arXiv  Detail & Related papers  (2024-09-09T07:13:44Z)
• The Kosmosis Use-Case of Crypto Rug Pull Detection and Prevention [0.0]
  Current methods to prevent crypto asset fraud are based on the analysis of transaction graphs within blockchain networks. We propose the Kosmosis approach, which aims to incrementally construct a knowledge graph as new blockchain and social media data become available.
  arXiv  Detail & Related papers  (2024-05-30T07:17:57Z)
• 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)
• Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0 [59.94605620983965]
  We design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0. To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model. Considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory.
  arXiv  Detail & Related papers  (2024-03-20T01:58:38Z)
• 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)
• Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts [26.70294159598272]
  This paper presents a novel collaborative learning framework designed to detect attacks in blockchain transactions and smart contracts. Our framework exhibits the capability to classify various types of blockchain attacks, including intricate attacks at the machine code level. Our framework achieves a detection accuracy of approximately 94% through extensive simulations and 91% in real-time experiments with a throughput of over 2,150 transactions per second.
  arXiv  Detail & Related papers  (2023-08-30T07:17:20Z)
• PTTS: Zero-Knowledge Proof-based Private Token Transfer System on Ethereum Blockchain and its Network Flow Based Balance Range Privacy Attack Analysis [0.0]
  We propose a Private Token Transfer System (PTTS) for the public blockchain. For the proposed framework, zero-knowledge based protocol has been designed using Zokrates and integrated into our private token smart contract. In the second part of the paper, we provide security and privacy analysis including the replay attack and the balance range privacy attack.
  arXiv  Detail & Related papers  (2023-08-29T09:13:31Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers [58.802352477207094]
  We explore the great potential of a pre-trained vision Transformer (ViT) to bridge the vast distribution gap between two modalities. We propose a mask modeling strategy that randomly masks a specific modality of some tokens to enforce the interaction between tokens from different modalities interacting proactively. Experiments demonstrate that our plug-and-play training augmentation techniques can significantly boost state-of-the-art one-stream and two trackersstream to a large extent in terms of both tracking precision and success rate.
  arXiv  Detail & Related papers  (2023-07-09T08:58:47Z)
• 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)
• TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts [2.3112192919085826]
  Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts. We find that TSGNs can provide more potential information to benefit the identification of phishing accounts.
  arXiv  Detail & Related papers  (2021-04-18T08:12:51Z)

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.