Machine learning based detection system for Ransomware classification of Bitcoin Transactions
Bibliographic record
Abstract
Abstract: Ransomware attacks are increasingly prevalent, posing a severe threat to global cybersecurity. Cybercriminals use cryptocurrencies, particularly Bitcoin, to collect ransom payments, capitalizing on their pseudo-anonymous and decentralized properties to evade detection and law enforcement. This study addresses the detection of Bitcoin transactions linked to ransomware using the BitcoinHeist dataset, which includes transactions from 28 ransomware families categorized as Princeton, Montreal, Padua, and legitimate 'white' transactions. We introduce a hybrid machine learning framework integrating supervised and semi-supervised approaches. The supervised component employs a stacking ensemble with XGBoost, Random Forest, Decision Trees and as base learners and a Multilayer Perceptron as the meta-learner. The semi-supervised component uses Labeled K-means and biased classifiers to identify previously unseen ransomware instances. Hyperparameter optimization via Optuna ensures model robustness. Evaluated through comprehensive metrics, including accuracy, precision, recall, F1-score, ROC score, and prediction time, our approach achieves a state-of-the-art accuracy of 97% in classifying known ransomware transactions and demonstrates robust performance in detecting unknown ransomware within the BitcoinHeist dataset. This research advances efforts to mitigate ransomware’s impact on the cryptocurrency ecosystem. . IndexTerms – Ransomware, Bitcoin transactions, Anamoly Detection , Machine Learning
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".