Machine learning-based ransomware classification of Bitcoin transactions

Ransomware presents a significant threat to the security and integrity of cryptocurrency transactions. This research paper explores the intricacies of ransomware detection in cryptocurrency transactions using the Bitcoinheist dataset. The dataset encompasses 28 distinct families classified into three ransomware categories: Princeton, Montreal, and Padua, along with a white category representing legitimate transactions. We propose a novel hybrid supervised and semi-supervised multistage machine learning framework to tackle this challenge. Our framework effectively classifies known ransomware families by leveraging ensemble learning techniques such as Decision Tree, Random Forest, XGBoost, and Stacking. Additionally, we introduce a novel semisupervised approach to accurately identify previously unseen ransomware instances within the dataset. Through rigorous evaluation employing comprehensive classification metrics, including accuracy, precision, recall, F1 score, RoC score, and prediction time, our proposed approach demonstrates promising results in ransomware detection within cryptocurrency transactions.