As the use of Blockchain for digital payments continues to rise in popularity, it also becomes susceptible to various malicious attacks. Successfully detecting anomalies within Blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in Blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating eXplainable Artificial Intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The Shapley Additive exPlanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we have introduced an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances TPR and ROC-AUC scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and FPR scores.

翻译：随着区块链在数字支付领域的应用日益普及，其也面临多种恶意攻击的威胁。成功检测区块链交易中的异常对于增强数字支付信任至关重要。然而，区块链交易数据中的异常检测任务因非法交易发生频率较低而具有挑战性。尽管已有多个相关研究，但仍存在局限性：缺乏对模型预测结果的解释机制。本研究通过将可解释人工智能（XAI）技术与异常规则融入基于树的集成分类器，旨在突破这一局限，实现比特币异常交易的检测。采用Shapley加法解释（SHAP）方法量化各特征贡献度，该方法与集成模型兼容。此外，我们提出了用于判定比特币交易是否异常的规则。同时设计了一种名为XGBCLUS的欠采样算法，用于平衡异常与非异常交易数据，并将其与其他常用欠采样与过采样技术进行对比。最后，比较了多种基于树的单一分类器与堆叠集成、投票集成分类器的性能。实验结果表明：（i）与现有最优的欠采样及过采样技术相比，XGBCLUS在TPR和ROC-AUC指标上均有提升；（ii）在准确率、TPR和FPR指标上，本文提出的集成分类器优于传统的单一基于树的机器学习分类器。