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Enhancing phishing detection with dynamic optimization and character-level deep learning in cloud environments.

PeerJ. Computer scienceยท2025
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Updated: May 20, 2025

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Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian

Vasavi Chithanuru1, Mangayarkarasi Ramaiah1

  • 1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, India.

Peerj. Computer Science
|March 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an ensemble stacking model for enhanced Ethereum blockchain security, achieving 99.6% accuracy in detecting fraudulent transactions. The explainable AI integration ensures transparent and reliable threat mitigation within the ecosystem.

Keywords:
Bayesian optimizationEnsemble stacking classifierEthereumFraud detectionMachine learning algorithmsSMOTEENN

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Blockchain technology, particularly Ethereum, offers secure and transparent transactions but faces significant security threats from cyber attackers.
  • Vulnerabilities include phishing, Ponzi schemes, eclipse attacks, Sybil attacks, and distributed denial of service (DDoS) incidents, necessitating robust detection mechanisms.
  • Existing security solutions often lack interpretability, hindering trust and adoption within the blockchain ecosystem.

Purpose of the Study:

  • To develop and evaluate a novel ensemble stacking model for detecting suspicious activities and potential threats on the Ethereum blockchain.
  • To enhance the model's predictive accuracy through Bayesian optimization and improve transparency using explainable artificial intelligence (XAI) tools.
  • To provide a reliable and interpretable solution for bolstering blockchain security and fortifying the Ethereum ecosystem against cyber threats.

Main Methods:

  • An ensemble stacking model was constructed, integrating Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a neural network (NN).
  • Bayesian optimization was employed for fine-tuning the ensemble model's hyperparameters to maximize predictive performance.
  • Explainable AI (XAI) techniques, including SHAP, LIME, and ELI5, were utilized to provide interpretable insights into model predictions.
  • A dataset of 9,841 Ethereum transactions, reduced to 17 key features, was used for training and validation.

Main Results:

  • The proposed ensemble stacking model achieved a high accuracy rate of 99.6% in identifying fraudulent Ethereum transactions.
  • The model demonstrated superior performance compared to other state-of-the-art methods in threat detection.
  • XAI tools provided clear feature importance and interpretability, enhancing the transparency of the detection process.

Conclusions:

  • The XAI-enabled ensemble stacking model presents a highly effective and interpretable solution for enhancing blockchain security on the Ethereum platform.
  • This approach significantly strengthens trust and reliability within the Ethereum ecosystem by accurately identifying and explaining security threats.
  • The findings highlight the potential of integrating ensemble methods with XAI for robust and transparent cybersecurity in decentralized systems.