Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis  

作　　者：Mohammad Hasan Mohammad Shahriar Rahman Helge Janicke Iqbal H.Sarker 

机构地区：[1]Department of Computer Science and Engineering,Premier University,4000 Chitagong,Bangladesh [2]Department of Computer Science and Engineering,United International University,1212 Dhaka,Bangladesh [3]Cyber Security Cooperative Research Centre,6027 Perth,Australia [4]Centre for Securing Digital Futures,Edith Cowan University,6027 Perth,Australia

出　　处：《Blockchain(Research and Applications)》2024年第3期106-122,共17页区块链研究（英文）

摘　　要：As the use of blockchain for digital payments continues to rise,it 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 introduce 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 true positive rate(TPR)and receiver operating characteristic-area under curve(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 false positive rate(FPR)scores.

关 键 词：Anomaly detection Blockchain Bitcoin transactions Data imbalance Data sampling Explainable AI Machine learning Decision tree Anomaly rules 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP181[自动化与计算机技术—计算机科学与技术]