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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 45 Documents
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Dynamic Relationship Analysis Between Gas Used and Gas Price in Ethereum Using VAR and Granger Causality Tests S Murugesan; S. Ramalingam; R. Elavarasi; P. Kanimozhi
Journal of Current Research in Blockchain Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i2.65

Abstract

his study investigates the dynamic relationship between network activity and transaction fees in the Ethereum blockchain by analysing the interaction between Gas Used and Gas Price through a multivariate time series model. The objective is to determine whether variations in network demand influence short-term gas price fluctuations. Daily data of Gas Used and Gas Price were transformed into different logarithmic forms to ensure stationarity. The Augmented Dickey–Fuller test confirmed that both variables are stationary at the five percent significance level, with ADF statistics of −6.21 for Δlog (Gas Used) and −7.12 for Δlog (Gas Price), and p-values below 0.001. The Vector Autoregression model was estimated with an optimal lag length of fourteen days, selected using the Akaike Information Criterion, reflecting the persistence of network and fee dynamics. The results of the Granger causality test indicate a unidirectional causal relationship from Gas Used to Gas Price, with an F-statistic of 3.72 and a p-value of 0.018, suggesting that fluctuations in network demand significantly precede changes in gas pricing. The reverse direction is not significant, with an F-statistic of 1.26 and a p-value of 0.28, indicating that transaction fees do not predict network activity. The impulse response analysis shows that a one standard deviation shock in Gas Used increases Gas Price for two to three days before returning to equilibrium, while shocks in Gas Price have minimal effects on Gas Used. These findings confirm that Ethereum’s fee market operates primarily as a demand-driven mechanism were congestion and transaction volume shape short-term gas price movements.
Enhancing Blockchain Security Through Smart Contract Vulnerability Classification Using BiLSTM and Attention Mechanism Abdulnaser Almasloum
Journal of Current Research in Blockchain Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i2.66

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.
Clustering Blockchain Wallet Behaviour Using K-Means and DBSCAN for Risk Profiling and Address Segmentation Raed Ghanem
Journal of Current Research in Blockchain Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i2.67

Abstract

Blockchain networks generate high-dimensional transactional data with diverse and irregular wallet behaviours. Understanding these behavioural patterns is essential for improving security monitoring, anomaly detection, and risk assessment within decentralized systems. This study applies two unsupervised machine learning algorithms, K-Means and DBSCAN, to analyse 303 blockchain wallet records using key attributes such as BlockHeight, UnixTimestamp, Block Density, Coin Day Weight, and Stake Distribution Rate. K-Means successfully identified three distinct behavioural clusters consisting of Cluster 1 with 200 wallets, Cluster 2 with 100 wallets, and Cluster 0 with 3 highly anomalous wallets. Numerical analysis revealed clear differences across clusters, including mean BlockHeight values of 5.5 million for Cluster 1, 15.4 million for Cluster 2, and 10.9 million for Cluster 0, along with Block Density percentages of 19.35, 48.90, and 60.00, respectively. DBSCAN further exposed behavioural complexity by detecting more than 90 noise points that represent irregular or outlier activity patterns and several small micro-clusters not captured by K-Means. PCA visualizations confirmed strong separation between clusters and highlighted the unique positioning of anomalous wallets. The combined use of centroid-based and density-based clustering provides a robust analytical foundation for profiling blockchain wallet behaviour, supporting more effective anomaly detection, risk classification, and address segmentation.
Decoding User Trust in Crypto Wallets with a BERT–XGBoost Hybrid Model for Multilingual Phantom Review Analysis R. Elavarasi; P. Gajalakshmi; S. Murugesan; K. Srinivasan
Journal of Current Research in Blockchain Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i2.68

Abstract

The rapid expansion of decentralized financial applications has increased the importance of understanding user trust in crypto wallet platforms. This study examines trust expressions in multilingual Phantom Wallet reviews using a hybrid classification framework that integrates BERT-based contextual embeddings with an XGBoost model. A total of 12,422 English and Indonesian reviews were collected and processed to construct a multilingual dataset for trust analysis. Exploratory findings reveal a highly polarized distribution of user ratings, indicating that trust in crypto wallets is strongly influenced by clear satisfaction or dissatisfaction rather than moderate evaluations. Cross-linguistic analysis indicates that Indonesian users express a higher proportion of low-trust reviews compared to English users, suggesting greater sensitivity to transaction errors and perceived asset safety concerns. Lexical patterns demonstrate that positive trust is associated with usability and performance stability, while negative trust is primarily driven by system failures, delays, and missing balance incidents. The results confirm that the BERT–XGBoost hybrid model is well-suited for decoding trust-related signals by combining contextual semantic understanding with structured metadata. This study contributes to the broader discourse on digital trust within Web3 environments by demonstrating an effective multilingual machine learning approach for analysing user perceptions in decentralized financial technologies.
Anomaly Detection in Blockchain Transactions Using Isolation Forest and Autoencoder Deep Learning Models Heru Supriyanto; Murtiyoso; Nilasari
Journal of Current Research in Blockchain Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i2.69

Abstract

Blockchain technology enables decentralized and transparent digital transactions, yet its open architecture also increases vulnerability to fraudulent and irregular activities. This study evaluates the effectiveness of the Isolation Forest method for detecting anomalous patterns within blockchain transaction data. A simulated dataset consisting of 10,130 transactions was constructed, including 62 injected anomalies that represent realistic irregular behaviours such as unusually large transaction values, extreme gas price spikes, and rapid consecutive transfers by a single sender. After applying feature engineering to capture temporal frequency, transaction dynamics, sender and receiver behaviour, and gas-related attributes, the Isolation Forest model was trained and evaluated using the embedded anomaly labels. The model achieved a precision of 0.4516, a recall of 0.4516, and an F1 score of 0.4516, indicating moderate detection capability. Analysis of the confusion matrix and anomaly score distribution further revealed overlapping characteristics between rare but legitimate transactions and true anomalies, which contributed to misclassification. Overall, the findings suggest that Isolation Forest can serve as an early anomaly filtering mechanism, although additional contextual information or hybrid detection strategies are needed to enhance performance in real blockchain environments.

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