Detecting Anomalies in Blockchain Transactions using Machine Learning
  Classifiers and Explainability Analysis
Mohammad Hasan Mohammad Shahriar Rahman Helge Janicke Iqbal H. Sarker
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.