Graph Network Models To Detect Illicit Transactions In Block Chain

• URL: http://arxiv.org/abs/2410.07150v1
• Date: Mon, 23 Sep 2024 04:38:44 GMT
• Title: Graph Network Models To Detect Illicit Transactions In Block Chain
• Authors: Hrushyang Adloori, Vaishnavi Dasanapu, Abhijith Chandra Mergu,
• Abstract summary: cryptocurrencies have led to an increase in illicit activities such as money laundering. We propose a novel approach to tackling this problem by applying graph attention networks with residual network-like architecture (GAT-ResNet) Our results demonstrate that the GAT-ResNet model has a potential to outperform the existing graph network models in terms of accuracy, reliability and scalability.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of cryptocurrencies has led to an increase in illicit activities such as money laundering, with traditional rule-based approaches becoming less effective in detecting and preventing such activities. In this paper, we propose a novel approach to tackling this problem by applying graph attention networks with residual network-like architecture (GAT-ResNet) to detect illicit transactions related to anti-money laundering/combating the financing of terrorism (AML/CFT) in blockchains. We train various models on the Elliptic Bitcoin Transaction dataset, implementing logistic regression, Random Forest, XGBoost, GCN, GAT, and our proposed GAT-ResNet model. Our results demonstrate that the GAT-ResNet model has a potential to outperform the existing graph network models in terms of accuracy, reliability and scalability. Our research sheds light on the potential of graph related machine learning models to improve efforts to combat financial crime and lays the foundation for further research in this area.

Related papers

• Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks [50.19590901147213]
  Graph neural networks (GNNs) have become instrumental in diverse real-world applications, offering powerful graph learning capabilities. GNNs are vulnerable to adversarial attacks, including membership inference attacks (MIA) This paper proposes an effective two-stage defense, Graph Transductive Defense (GTD), tailored to graph transductive learning characteristics.
  arXiv  Detail & Related papers  (2024-06-12T06:36:37Z)
• Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification [68.86863899919358]
  We introduce a groundbreaking approach to protect GNN models in Machine Learning from model-centric attacks. Our approach includes a comprehensive verification schema for GNN's integrity, taking into account both transductive and inductive GNNs. We propose a query-based verification technique, fortified with innovative node fingerprint generation algorithms.
  arXiv  Detail & Related papers  (2023-12-13T03:17:05Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• Efficient Network Representation for GNN-based Intrusion Detection [2.321323878201932]
  The last decades have seen a growth in the number of cyber-attacks with severe economic and privacy damages. We propose a novel network representation as a graph of flows that aims to provide relevant topological information for the intrusion detection task. We present a Graph Neural Network (GNN) based framework responsible for exploiting the proposed graph structure.
  arXiv  Detail & Related papers  (2023-09-11T16:10:12Z)
• Finding Money Launderers Using Heterogeneous Graph Neural Networks [0.0]
  This paper introduces a graph neural network (GNN) approach to identify money laundering activities within a large heterogeneous network. We extend the homogeneous GNN method known as the Message Passing Neural Network (MPNN) to operate effectively on a heterogeneous graph. Our findings highlight the importance of using an appropriate GNN architecture when combining information in heterogeneous graphs.
  arXiv  Detail & Related papers  (2023-07-25T13:49:15Z)
• 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)
• 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)
• Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture [14.435076554010985]
  We first present a novel Directed Dynamic Snapshot (DDS) linkage design for graph construction and a Lambda Neural Networks (LNN) architecture for effective inference with Graph Neural Networks embeddings. Experiments show that our LNN on graph, outperforms baseline models significantly and is computational efficient for real-time fraud detection.
  arXiv  Detail & Related papers  (2021-10-09T12:57:18Z)
• Blockchain Phishing Scam Detection via Multi-channel Graph Classification [1.6980621769406918]
  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.
  arXiv  Detail & Related papers  (2021-08-19T02:59:55Z)
• 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)
• 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)

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.