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Stream/Area:
-
Subject:
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Paper ID:
IJSREM66543
Volume:
10 |
8 |
Aug 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Artificial Intelligence & Machine Learning
CMR Institute of Technology,Hyderabad
Department of Artificial Intelligence & Machine Learning
CMR Institute of Technology,Hyderabad
Department of Artificial Intelligence & Machine Learning
CMR Institute of Technology,Hyderabad
Department of Artificial Intelligence & Machine Learning
CMR Institute of Technology,Hyderabad
Department of Artificial Intelligence & Machine Learning
CMR Institute of Technology,Hyderabad
Bitcoin has a reputation of being used for unlawful activities, such as money laundering, dark web transactions, and payments for ransomware in the context of smart cities. Blockchain technology prevents illegal transactions, but cannot detect these transactions. Anomaly detection is a fundamental technique for recognizing potential fraud. The heuristic and signature-based approaches were the foundation of earlier detection techniques, but tragically, these methods were in sufficient to explore the entire complexity of anomaly detection. Machine Learning (ML) is a promising approach to anomaly detection, as it can be trained on large datasets of known malware samples to identify patterns and features of the transactions. Researchers are focusing on determining an efficient fraud and security threat detection model that overcomes the drawbacks of the existing methods. Therefore, ensemble learning can be applied to anomaly detection in Bitcoin by combining multiple ML classifiers. In the proposed model, the ADASYN-TL (Adaptive Synthetic + Tomek Link) balancing technique is used for data balancing. Random search, grid search and Bayesian optimization are used for hyperparameter tuning. The hyperparameters have a great impact on the performance of the model. For classification, we used the stacking model by combining Decision Tree, Naive Bayes, K-Nearest Neighbors, and Random Forest. We used Shapley Additive explanation (SHAP) to interpret the predictions of the stacking model. The model also explores the performance of different classifiers using accuracy, F1-score, Area Under Curve-Receiver Operating Characteristic (AUC-ROC), precision, recall, False Positive Rate (FPR) and execution time, and ultimately selects the ideal model.
KEYWORDS
This study aimed to develop an effective fraud detection model for Bitcoin transactions by addressing class imbalance, overfitting issues, and hyperparameter sensitivity. The proposed ensemble stacking framework combined hybrid balancing techniques (ADASYN-TL and SMOTE-ENN), hyperparameter tuning (Random Search, Grid Search, Bayesian Optimization), and multiple machine learning classifiers. The experimental results showed that the stacking model achieved high detection performance with 97% accuracy, 96% precision, 98% recall, 97% F1-score, and 99% AUC-ROC, while maintaining a low false positive rate of 3%. By combining multiple classifiers, the system improved generalization and reduced misclassification compared to individual models. Therefore, the research successfully achieved its objective and provided a reliable and practical solution for fraud detection in Bitcoin transactions.
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Publication Date
8/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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