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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Current Research in Blockchain International Journal Research on Metaverse
Brzozowska, Anna
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Classification of Bitcoin Ransomware Transactions Using Random Forest: A Data Mining Approach for Blockchain Security Emary, Ibrahiem M. M. El; Brzozowska, Anna; Popławski, Łukasz; Dziekański, Paweł; Glova, Jozef
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.33

Abstract

The rapid evolution of ransomware attacks necessitates robust and scalable detection mechanisms to safeguard digital assets. This study leverages the Bitcoin Ransomware Dataset, comprising 2,916,697 transactions, to evaluate the effectiveness of the Random Forest algorithm in classifying ransomware-related activities. Through comprehensive preprocessing, including feature encoding and standardization, and exploratory data analysis (EDA), the dataset is prepared for modeling. The Random Forest model achieves an overall accuracy of 99%, demonstrating exceptional performance in identifying the majority class. However, challenges persist in classifying minority classes, highlighting the impact of class imbalance. Feature importance analysis reveals that attributes such as income, weight, and length play pivotal roles in the classification process. The study underscores the potential of Random Forest for ransomware detection while emphasizing the need for advanced techniques to address class imbalance and improve minority class performance.
Anomaly Detection in Blockchain-Based Metaverse Transactions Using Hybrid Autoencoder and Isolation Forest Models for Risk Identification and Behavioral Pattern Analysis El Emary, Ibrahiem M. M.; Brzozowska, Anna; Popławski, Łukasz; Dziekański, Paweł; Glova, Jozef
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.45

Abstract

The increasing complexity of transactions within blockchain-based metaverse ecosystems has intensified the need for robust anomaly detection systems capable of identifying fraudulent, automated, or irregular behaviors. This study proposes a Hybrid Autoencoder–Isolation Forest (AE–IF) model for detecting anomalies in metaverse blockchain transactions through a combination of deep feature reconstruction and ensemble-based isolation. The proposed framework leverages the Autoencoder’s ability to learn nonlinear feature representations and the Isolation Forest’s capacity to isolate sparse anomalies, enabling the detection of both global and local irregularities. Experimental evaluation using real-world transaction data demonstrates that the hybrid model outperforms individual methods, achieving a ROC-AUC of 0.952, Precision of 0.88, Recall of 0.86, and F1-Score of 0.87. The ROC and Precision–Recall analyses confirm the model’s superior discriminative power and stability across imbalanced data distributions. Furthermore, behavioral analysis reveals distinct high-risk transaction patterns, including extended user sessions, cross-regional fund transfers, and irregular purchase behaviors. The results highlight the hybrid model’s effectiveness not only in anomaly detection but also in uncovering underlying behavioral and geographical risk factors. The proposed framework provides a scalable foundation for intelligent financial risk monitoring and cyber-fraud detection in decentralized metaverse economies.
Co-Authors Dziekański, Paweł El Emary, Ibrahiem M. M. Emary, Ibrahiem M. M. El Glova, Jozef Popławski, Łukasz