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September 10, 2025Ontology of DesigningOpen Access

Detection of anomalous cryptocurrency transactions using neural networks and ontologies

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Authors

IKIgor KotenkoDLDmitry LevshunKZKsenia Zhernova

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Overview

Neural network models detect anomalous transactions in cryptocurrency, suggesting enhanced security measures.

Key Points

  • Detection accuracy of 0.94 highlights the effectiveness of the gated recurrent units model, outperforming other neural approaches.
  • Results indicate that the proposed neural network method improves identification of illicit cryptocurrency transactions compared to traditional algorithms.
  • Experimental studies utilized a dataset of cryptocurrency transactions while comparing performance with both neural and non-neural classifiers.
  • The approach's novelty stems from integrating statistical characteristics from transaction graphs with deep learning techniques.

Cite This Study

Kotenko et al. (2025) studied this question.

synapsesocial.com/papers/68c1bd4254b1d3bfb60eed16https://doi.org/10.18287/2223-9537-2025-15-3-334-350
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Survey of Deep Learning-Based Methods for Detecting Anomalous Transactions in Blockchain2025
  2. 2AI-Driven Fraud Detection in Financial Transactions with Graph Neural Networks and Anomaly Detection2024 · 26 citations
  3. 3Data-Centric Generative and Adaptive Detection Framework for Abnormal Transaction Prediction2026 · 1 citations
  4. 4Neighborhood Subgraph-Based Illicit Transaction Detection in Cryptocurrency Networks2025
  5. 5Unsupervised Machine Learning Based Anomaly Detection in High Frequency Data: Evidence from Cryptocurrency Market2025