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Journal of Current Research in Blockchain
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30481430     DOI : https://doi.org/10.47738/jcrb
Core Subject : Economy, Science,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
The Journal of Current Research in Blockchain publishes high-quality research on: Blockchain technology Smart Contract Data Privacy Decentralization Data Distributed Ledger Technology Decentralized Applications Our goal is to provide a platform for researchers, practitioners, and policymakers to share innovative findings, discuss emerging trends, and address the challenges and opportunities presented by blockchain technology across various sectors.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 3 No. 1 (2026): Regular Issue March 2026" : 5 Documents clear
Network-Based Risk Scoring of Blockchain Nodes Using Graph Neural Networks (GNN) Widodo, Slamet; Afuan, Lasmedi
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.54

Abstract

Blockchain technology has introduced a decentralized and transparent mechanism for recording transactions; however, the increasing volume and interconnectivity of blockchain networks also raise the risk of fraudulent and high-risk activities. This study proposes a Graph Neural Network (GNN)-based framework to evaluate the risk levels of blockchain nodes by integrating both transactional attributes and structural relationships. Using a dataset of 10,000 blockchain records and approximately 412,000 edges, the network was modelled as a graph in which each node represents an address and edges denote transaction or similarity links. As baselines, Random Forest and XGBoost models were employed, achieving accuracies of 0.94 and 0.95, respectively, with F1-scores of 0.93 and 0.94. These models effectively captured individual node patterns but lacked awareness of inter-node dependencies. The proposed GNN model demonstrated the highest overall performance, with an accuracy of 0.96 and an F1-score of 0.95, by learning from both node attributes and their topological context. This approach enabled the identification of high-risk nodes that traditional models failed to detect. The results confirm that network-based learning significantly enhances the accuracy and interpretability of blockchain risk analysis. The proposed GNN framework provides a scalable foundation for real-time blockchain monitoring, anomaly detection, and governance systems, contributing to improved transparency and resilience within decentralized financial ecosystems.
Network-Based Anomaly Detection in Blockchain Transactions Using Graph Neural Network (GNN) and DBSCAN Guballo, Jayvie Ochona; Andes, Joy April C.
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.55

Abstract

The increasing volume of blockchain transactions has raised significant concerns regarding the detection of irregular and high-risk activities within decentralized financial ecosystems. Conventional anomaly detection approaches tend to focus on transactional values alone, often neglecting the structural relationships that define user interactions. This study introduces a network-based anomaly detection framework that integrates graph embedding and density-based clustering techniques to identify abnormal transaction behaviours. Using a real-world blockchain transaction dataset consisting of 1,316 unique addresses (nodes) and 2,709 transaction links (edges), a directed network model was constructed to represent the flow of digital assets between users. A Singular Value Decomposition (SVD)-based graph embedding was employed to map network structures into a two-dimensional latent space, followed by DBSCAN clustering to isolate low-density outliers. The results indicate that approximately 34 nodes, or 2.6% of the total, were classified as anomalous, exhibiting unusually high transaction volumes, disproportionate connectivity, or bridging characteristics across distinct communities. These findings demonstrate that combining topological representation learning with unsupervised clustering effectively reveals hidden patterns of irregularity within blockchain networks. The proposed framework provides a computationally efficient and interpretable foundation for future integration with advanced graph learning models, such as Graph Neural Networks (GNN), to enhance fraud detection and risk assessment in decentralized systems.
Enhancing Blockchain Security Through Smart Contract Vulnerability Classification Using BiLSTM and Attention Mechanism Rahardja, Untung; Aini, Qurotul
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.56

Abstract

The rapid adoption of blockchain technology has intensified the need for robust smart contract security mechanisms. However, traditional rule-based or static analysis tools often fail to detect context-dependent vulnerabilities embedded in complex contract logic. This study proposes a deep learning framework for automated smart contract vulnerability classification using a Bidirectional Long Short-Term Memory (BiLSTM) network integrated with an Attention Mechanism. The model was trained and evaluated on the SC_Vuln_8label.csv dataset, comprising 12,520 labelled Solidity smart contracts categorized into eight distinct vulnerability types, including Re-entrancy, Integer Overflow, and Short Address Attack. Through bidirectional contextual learning and attention-based feature weighting, the proposed model achieved 93.7% test accuracy, 0.93 precision, and a macro F1-score of 0.92, outperforming baseline models such as CNN, GRU, and standard LSTM by up to 5.3 percentage points. Attention heatmap analysis further revealed the model’s interpretability by highlighting vulnerability-prone code segments (e.g., call.value, send(), and withdraw() functions) consistent with expert-identified risk indicators. These results demonstrate that the BiLSTM + Attention framework not only enhances vulnerability detection accuracy but also provides transparent and explainable reasoning, offering a reliable foundation for AI-assisted smart contract auditing systems in blockchain security.
A Hybrid Ensemble Framework Combining Transformer Networks, CNN-LSTM, and Prophet for Multi-Horizon Bitcoin Price Prediction Using 1-Minute Time Series Data Maidin, Siti Sarah; Hemalatha, M.; Sun, Jing
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.57

Abstract

Bitcoin price forecasting at one-minute frequency presents significant challenges due to rapid volatility and noise in high-frequency markets. This study proposes a hybrid ensemble framework integrating a CNN-LSTM model, a Transformer architecture, and a Prophet-based component to perform multi-horizon prediction using 500,000 one-minute BTC/USD observations. The model is evaluated across 5-minute, 15-minute, and 30-minute horizons. The results show that the ensemble achieves the best performance for the 5-minute horizon with MAE = 41.565 USD, RMSE = 60.722 USD, and MAPE = 0.156. This outperforms the CNN-LSTM model (MAE = 47.838 USD) and the Transformer model (MAE = 53.733 USD). Performance decreases at the 15-minute horizon due to Transformer instability, where the ensemble reaches MAE = 269.347 USD and the Transformer reaches MAE = 530.429 USD. At the 30-minute horizon, performance stabilizes, with the ensemble producing MAE = 84.481 USD, close to the CNN-LSTM result (MAE = 84.186 USD) and better than the Transformer (MAE = 153.887 USD). These findings indicate that the hybrid ensemble is highly effective for ultra-short-term forecasting but requires horizon-specific tuning to remain stable for medium-range intervals.
Modeling Financial Volatility of S&P 500 ETF Using GARCH and Rolling Window Analysis Yang, Wang; Fan, Chen
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.58

Abstract

This study investigates the financial volatility of the SPDR S&P 500 ETF (SPY) using two distinct approaches the Rolling Window Volatility (20-day) and the GARCH (1,1) Approximation to analyze and compare the dynamic behavior of market risk. The analysis utilizes daily SPY price data to compute logarithmic returns and model volatility persistence over time. Descriptive statistics indicate that SPY returns exhibit volatility clustering, leptokurtosis, and negative skewness, implying that extreme market movements occur more frequently than predicted by a normal distribution. Empirical results show that both volatility measures successfully capture the cyclical nature of market risk but differ in responsiveness and interpretability. The rolling window method provides an intuitive and historical view of volatility patterns, while the GARCH (1,1) model captures conditional and time-varying volatility more effectively by incorporating both short-term shocks and long-term persistence. Comparative analysis reveals that GARCH estimates produce smoother and more adaptive volatility dynamics, making them more suitable for forecasting and real-time risk assessment. Overall, the findings confirm that volatility in financial markets is not constant but evolves dynamically in response to new information and investor behavior. The study emphasizes the importance of conditional volatility models in improving the accuracy of risk evaluation, portfolio management, and market forecasting, particularly during periods of heightened uncertainty.

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