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All Journal Journal of Current Research in Blockchain International Journal Research on Metaverse
Al-Rawahna, Ammar Salamh Mujali
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Unsupervised Anomaly Detection in Digital Currency Trading: A Clustering and Density-Based Approach Using Bitcoin Data Hariguna, Taqwa; Al-Rawahna, Ammar Salamh Mujali
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.12

Abstract

This study investigates the application of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for detecting anomalies in Bitcoin trading data. With the growing significance of Bitcoin in the financial market, identifying irregular trading patterns is crucial for maintaining market integrity and preventing market manipulation. Utilizing a dataset from Kaggle, which includes features such as date, timestamp, open, high, low, close, volume, and number of trades, the data was aggregated from minute-by-minute to hourly intervals for more manageable analysis. The DBSCAN algorithm effectively identified a primary cluster comprising 29,612 data points and flagged 2 points as anomalies, achieving a precision of 1.0, recall of 0.0068, F1-score of 0.0135, and an AUC-ROC of 0.5034. The optimal parameters, determined through sensitivity analysis, were epsilon (ε) = 0.1 and min_samples = 3, yielding the highest silhouette score of 0.21499. These results underscore the algorithm's ability to accurately label anomalies while highlighting the challenge of comprehensive anomaly detection. The study contributes to the field of financial anomaly detection by demonstrating the effectiveness of DBSCAN in analyzing high-dimensional, noisy datasets. It also addresses gaps in the literature regarding the application of density-based clustering methods to Bitcoin trading data. Despite its contributions, the study acknowledges limitations, such as potential data aggregation impact and the need for further validation with different datasets. Future research directions include integrating additional features like social media sentiment and exploring hybrid approaches that combine supervised and unsupervised methods.
Anomaly Detection in Open Metaverse Blockchain Transactions Using Isolation Forest and Autoencoder Neural Networks Buchdadi, Agung Dharmawan; Al-Rawahna, Ammar Salamh Mujali
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.20

Abstract

The study explores anomaly detection in blockchain transactions within the Open Metaverse, utilizing Isolation Forest and Autoencoder Neural Networks. With the rise of the Metaverse, blockchain technology has become essential for secure digital transactions. However, the decentralized nature of blockchain makes it vulnerable to various anomalies, potentially undermining trust and security in digital spaces. Isolation Forest, an unsupervised machine learning algorithm, isolates anomalies based on the assumption that anomalies are few and distinct from regular data points. Its effectiveness in handling high-dimensional data makes it suitable for real-time applications. On the other hand, Autoencoders, a type of neural network, excel in detecting anomalies through reconstruction error, identifying data points that deviate from normal patterns. The research applied these models to a simulated dataset from the Open Metaverse, including features like transaction amount, login frequency, and session duration, to capture nuanced user behaviors. Preprocessing steps, such as one-hot encoding for categorical features and standardization for numerical features, ensured data consistency for accurate modeling. The Isolation Forest achieved a precision of 0.85, while the Autoencoder slightly outperformed it with a precision of 0.87. Both models demonstrated strong AUC-ROC values, with the Autoencoder scoring 0.85 compared to Isolation Forest’s 0.82, indicating robust performance in distinguishing normal from anomalous transactions. The findings underscore the potential of both models to enhance security in blockchain-based virtual environments, with the Autoencoder showing an edge in handling complex data patterns. However, the use of simulated data presents limitations, suggesting the need for further testing with real-world Metaverse transaction data. Future research could explore integrating other advanced algorithms, such as Graph Neural Networks, to improve anomaly detection in blockchain systems.
Co-Authors Agung Dharmawan Buchdadi Taqwa Hariguna