使用支持xai的集成堆栈和贝叶斯优化来主动检测以太坊账户中的异常行为。

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
PeerJ Computer Science Pub Date : 2025-03-19 eCollection Date: 2025-01-01 DOI:10.7717/peerj-cs.2630
Vasavi Chithanuru, Mangayarkarasi Ramaiah
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引用次数: 0

摘要

以以太坊平台为代表的区块链技术的去中心化、开源架构,通过实现安全和透明的交换,改变了在线交易。然而,这种体系结构也使网络暴露于各种安全威胁之下,网络攻击者可以利用这些威胁。检测以太坊区块链账户中的可疑行为可以帮助减轻攻击,包括网络钓鱼、庞氏骗局、eclipse攻击、Sybil攻击和分布式拒绝服务(DDoS)事件。该系统引入了一个集成堆叠模型,结合随机森林(RF)、极限梯度增强(XGBoost)和神经网络(NN)来检测以太坊平台内的潜在威胁。集成模型使用贝叶斯优化进行微调,以提高预测精度,而可解释的人工智能(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。使用的数据集包括52个初始字段(减少到17个相关特征)中的9,841笔以太坊交易,包括合法和欺诈记录。实验结果表明,该模型的准确率达到了99.6%,优于其他前沿方法。这些发现表明,支持xai的集成堆叠模型为区块链安全性提供了一种高效、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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.

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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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