Blockchain Phishing Scam Detection via Multi-channel Graph Classification

• URL: http://arxiv.org/abs/2108.08456v1
• Date: Thu, 19 Aug 2021 02:59:55 GMT
• Title: Blockchain Phishing Scam Detection via Multi-channel Graph Classification
• Authors: Dunjie Zhang and Jinyin Chen
• Abstract summary: Phishing scam detection methods will protect possible victims and build a healthier blockchain ecosystem. We defined the transaction pattern graphs for users and transformed the phishing scam detection into a graph classification task. The proposed multi-channel graph classification model (MCGC) is more able to detect potential phishing by extracting the transaction pattern features of the target users.
• Score: 1.6980621769406918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the popularity of blockchain technology, the financial security issues of blockchain transaction networks have become increasingly serious. Phishing scam detection methods will protect possible victims and build a healthier blockchain ecosystem. Usually, the existing works define phishing scam detection as a node classification task by learning the potential features of users through graph embedding methods such as random walk or graph neural network (GNN). However, these detection methods are suffered from high complexity due to the large scale of the blockchain transaction network, ignoring temporal information of the transaction. Addressing this problem, we defined the transaction pattern graphs for users and transformed the phishing scam detection into a graph classification task. To extract richer information from the input graph, we proposed a multi-channel graph classification model (MCGC) with multiple feature extraction channels for GNN. The transaction pattern graphs and MCGC are more able to detect potential phishing scammers by extracting the transaction pattern features of the target users. Extensive experiments on seven benchmark and Ethereum datasets demonstrate that the proposed MCGC can not only achieve state-of-the-art performance in the graph classification task but also achieve effective phishing scam detection based on the target users' transaction pattern graphs.

Related papers

• 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)
• 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)
• 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)
• Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics [5.617291981476445]
  The paper analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. It shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution.
  arXiv  Detail & Related papers  (2022-06-07T16:22:55Z)
• TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection [11.20384152151594]
  Existing phishing scams detection technology mostly uses machine learning or network representation learning to mine the key information from the transaction network to identify phishing addresses. We propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing detection performance. Our TTAGN (92.8% AUC, and 81.6% F1score) outperforms the state-of-the-art methods, and the effectiveness of temporal edges representation and edge2node module is also demonstrated.
  arXiv  Detail & Related papers  (2022-04-28T12:17:00Z)
• Deep Fraud Detection on Non-attributed Graph [61.636677596161235]
  Graph Neural Networks (GNNs) have shown solid performance on fraud detection. labeled data is scarce in large-scale industrial problems, especially for fraud detection. We propose a novel graph pre-training strategy to leverage more unlabeled data.
  arXiv  Detail & Related papers  (2021-10-04T03:42:09Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)
• 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)
• Identity Inference on Blockchain using Graph Neural Network [5.5927440285709835]
  Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. We present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern. We also propose a generic end-to-end graph neural network model, named $textI2 textBGNN$, which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity.
  arXiv  Detail & Related papers  (2021-04-14T00:15:38Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)
• Adversarial Attack on Community Detection by Hiding Individuals [68.76889102470203]
  We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model.
  arXiv  Detail & Related papers  (2020-01-22T09:50:04Z)

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.