Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization
The decentralized, open-source architecture of blockchain technology, exemplified by the Ethereum platform, has transformed online transactions by enabling secure and transparent exchanges. However, this architecture also exposes the network to various security threats that cyber attackers can explo...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-03-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2630.pdf |
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| author | Vasavi Chithanuru Mangayarkarasi Ramaiah |
| author_facet | Vasavi Chithanuru Mangayarkarasi Ramaiah |
| author_sort | Vasavi Chithanuru |
| collection | DOAJ |
| description | The decentralized, open-source architecture of blockchain technology, exemplified by the Ethereum platform, has transformed online transactions by enabling secure and transparent exchanges. However, this architecture also exposes the network to various security threats that cyber attackers can exploit. Detecting suspicious behaviors in account on the Ethereum blockchain can help mitigate attacks, including phishing, Ponzi schemes, eclipse attacks, Sybil attacks, and distributed denial of service (DDoS) incidents. The proposed system introduces an ensemble stacking model combining Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a neural network (NN) to detect potential threats within the Ethereum platform. The ensemble model is fine-tuned using Bayesian optimization to enhance predictive accuracy, while explainable artificial intelligence (XAI) tools—SHAP, LIME, and ELI5—provide interpretable feature insights, improving transparency in model predictions. The dataset used comprises 9,841 Ethereum transactions across 52 initial fields (reduced to 17 relevant features), encompassing both legitimate and fraudulent records. The experimental findings demonstrate that the proposed model achieves a superior accuracy of 99.6%, outperforming that of other cutting-edge methods. These findings demonstrate that the XAI-enabled ensemble stacking model offers a highly effective, interpretable solution for blockchain security, strengthening trust and reliability within the Ethereum ecosystem. |
| format | Article |
| id | doaj-art-ab2859aea6844d8ea6eff03e0428ad6f |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-ab2859aea6844d8ea6eff03e0428ad6f2025-08-20T02:51:00ZengPeerJ Inc.PeerJ Computer Science2376-59922025-03-0111e263010.7717/peerj-cs.2630Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimizationVasavi ChithanuruMangayarkarasi RamaiahThe decentralized, open-source architecture of blockchain technology, exemplified by the Ethereum platform, has transformed online transactions by enabling secure and transparent exchanges. However, this architecture also exposes the network to various security threats that cyber attackers can exploit. Detecting suspicious behaviors in account on the Ethereum blockchain can help mitigate attacks, including phishing, Ponzi schemes, eclipse attacks, Sybil attacks, and distributed denial of service (DDoS) incidents. The proposed system introduces an ensemble stacking model combining Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a neural network (NN) to detect potential threats within the Ethereum platform. The ensemble model is fine-tuned using Bayesian optimization to enhance predictive accuracy, while explainable artificial intelligence (XAI) tools—SHAP, LIME, and ELI5—provide interpretable feature insights, improving transparency in model predictions. The dataset used comprises 9,841 Ethereum transactions across 52 initial fields (reduced to 17 relevant features), encompassing both legitimate and fraudulent records. The experimental findings demonstrate that the proposed model achieves a superior accuracy of 99.6%, outperforming that of other cutting-edge methods. These findings demonstrate that the XAI-enabled ensemble stacking model offers a highly effective, interpretable solution for blockchain security, strengthening trust and reliability within the Ethereum ecosystem.https://peerj.com/articles/cs-2630.pdfEthereumFraud detectionSMOTEENNMachine learning algorithmsBayesian optimizationEnsemble stacking classifier |
| spellingShingle | Vasavi Chithanuru Mangayarkarasi Ramaiah Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization PeerJ Computer Science Ethereum Fraud detection SMOTEENN Machine learning algorithms Bayesian optimization Ensemble stacking classifier |
| title | Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization |
| title_full | Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization |
| title_fullStr | Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization |
| title_full_unstemmed | Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization |
| title_short | Proactive detection of anomalous behavior in Ethereum accounts using XAI-enabled ensemble stacking with Bayesian optimization |
| title_sort | proactive detection of anomalous behavior in ethereum accounts using xai enabled ensemble stacking with bayesian optimization |
| topic | Ethereum Fraud detection SMOTEENN Machine learning algorithms Bayesian optimization Ensemble stacking classifier |
| url | https://peerj.com/articles/cs-2630.pdf |
| work_keys_str_mv | AT vasavichithanuru proactivedetectionofanomalousbehaviorinethereumaccountsusingxaienabledensemblestackingwithbayesianoptimization AT mangayarkarasiramaiah proactivedetectionofanomalousbehaviorinethereumaccountsusingxaienabledensemblestackingwithbayesianoptimization |