Chithanuru Vasavi, Ramaiah Mangayarkarasi
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, India.
PeerJ Comput Sci. 2025 Mar 19;11:e2630. doi: 10.7717/peerj-cs.2630. eCollection 2025.
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.
以以太坊平台为代表的区块链技术的去中心化、开源架构，通过实现安全透明的交易，改变了在线交易方式。然而，这种架构也使网络面临各种网络攻击者可能利用的安全威胁。检测以太坊区块链账户中的可疑行为有助于减轻攻击，包括网络钓鱼、庞氏骗局、日食攻击、女巫攻击和分布式拒绝服务（DDoS）事件。所提出的系统引入了一种集成堆叠模型，该模型结合了随机森林（RF）、极端梯度提升（XGBoost）和神经网络（NN），以检测以太坊平台内的潜在威胁。使用贝叶斯优化对集成模型进行微调，以提高预测准确性，而可解释人工智能（XAI）工具——SHAP、LIME和ELI5——提供可解释的特征洞察，提高模型预测的透明度。所使用的数据集包括9841笔以太坊交易，涉及52个初始字段（减少到17个相关特征），涵盖合法和欺诈记录。实验结果表明，所提出的模型实现了99.6%的卓越准确率，优于其他前沿方法。这些结果表明，启用XAI的集成堆叠模型为区块链安全提供了一种高效、可解释的解决方案，增强了以太坊生态系统内的信任和可靠性。
