ARCADE—Adversarially Robust Cost-Sensitive Anomaly Detection in Blockchain Using Explainable Artificial Intelligence

Abstract

Blockchain technology is increasingly being adopted across critical domains, such as healthcare and finance, yet it remains susceptible to anomalies and malicious attacks. Hence, robust anomaly detection is essential in these decentralized systems to maintain integrity, trust, and reliability. However, anomaly detection is still challenging due to data imbalances, adversarial resilience, and the lack of explanation in existing approaches. This work presents ARCADE, a novel approach for adversarially resilient anomaly detection in blockchain networks that leverages an optimized cost-sensitive stacking ensemble learning combined with explainable artificial intelligence (XAI) techniques. Firstly, the proposed approach uses cost-sensitive learning to address the data imbalance problem by optimizing class weights that are integrated with stacking ensemble learning to enhance detection accuracy. Secondly, along with this, newly engineered features are employed to strengthen the resilience of the model against malicious perturbations. Lastly, XAI techniques are applied to provide comprehensive insights and explanations for model prediction. To evaluate ARCADE, the Ethereum network transactions dataset is utilized to ensure a realistic case study. The experimental results show the superiority of the ARCADE in several aspects, achieving a high accuracy of 99.65%; strong resilience against adversarial perturbations, achieving an accuracy of 99.38% for low-intensity attacks, 91.04% for moderate attacks, and over 78% for extreme attacks; and surpassing existing techniques while also providing explainability for domain users.