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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 50 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.
AI-Driven Unsupervised Framework for Detecting and Analyzing Anomalous Cryptocurrency Transactions Across Blockchain Networks Using Multi-Feature Analysis Guevara Ananta Toer; Axel Sandi Seno; Najmal Falah
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

The rapid growth of cryptocurrency networks has increased the risk of fraudulent transactions, necessitating effective and scalable detection methods. This study presents an AI-driven framework for detecting anomalous transactions in cryptocurrency datasets using unsupervised learning techniques, specifically the Isolation Forest algorithm. A dataset of 50,000 transactions was analyzed, with 500 anomalies successfully identified based on multiple features including transaction amount, fees, sender and receiver addresses, mining pools, and currency type. The results show that anomalous transactions frequently exhibit extreme values and irregular patterns that are difficult to detect using traditional rule-based approaches. Multi-feature analysis enhances detection accuracy, enabling the prioritization of high-risk transactions for manual review or automated mitigation. These findings demonstrate that unsupervised AI methods provide a robust, adaptive, and scalable solution for proactive fraud detection in blockchain networks, with the potential to integrate deeper learning models for further improvement in detecting complex fraudulent behaviors.
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.
Towards Trustworthy Smart Contract Security: Explainable Multi-Class Vulnerability Detection Using Machine Learning Musbah J. Aqel
Journal of Current Research in Blockchain Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i3.74

Abstract

Smart contracts have become a fundamental component of blockchain ecosystems, enabling decentralized applications and automated transactions without intermediaries. However, vulnerabilities in smart contracts can lead to severe financial losses and security breaches, highlighting the need for effective automated vulnerability detection mechanisms. This study proposes an explainable machine learning framework for multi-class smart contract vulnerability detection using TF-IDF feature extraction, XGBoost classification, and SHAP (SHapley Additive exPlanations) analysis. The proposed approach was evaluated on a dataset containing 6,387 Solidity smart contracts categorized into eight vulnerability classes: Block Dependency (BD), Dangerous Delegatecall (DC), Front Running Exposure (FE), Integer Overflow/Underflow (IOU), Reentrancy (RE), Selfdestruct Exposure (SE), Timestamp Dependency (TD), and Unchecked Call (UC). Experimental results show that the XGBoost model achieved an average accuracy of 65.15% and a macro-F1 score of 65.14% under 5-fold stratified cross-validation, outperforming the Random Forest baseline across all evaluation metrics. Class-level analysis revealed strong detection performance for Dangerous Delegatecall, Integer Overflow/Underflow, and Timestamp Dependency vulnerabilities, while Reentrancy and Unchecked Call remained challenging due to their similar behavioral characteristics. Furthermore, SHAP-based explainability identified security-relevant Solidity tokens such as call, delegatecall, block number, and now as the most influential features contributing to vulnerability classification. These findings demonstrate that the proposed framework provides a transparent and effective approach for smart contract vulnerability detection, supporting the development of trustworthy blockchain security analysis systems.
Hybrid Feature Engineering and Explainable Ensemble Learning for Ethereum Smart Contract Vulnerability Detection Hushalictmy Paliyanny
Journal of Current Research in Blockchain Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i3.76

Abstract

Ethereum smart contracts have become a fundamental component of decentralized blockchain applications. However, vulnerabilities in Solidity smart contracts can lead to severe financial losses and security risks. Traditional vulnerability detection approaches often rely on opcode analysis or deep learning architectures with limited interpretability and high computational complexity. This study proposes a hybrid feature engineering and explainable ensemble learning framework for Ethereum smart contract vulnerability detection. The proposed framework integrates lexical TF-IDF features, structural software metrics, security-oriented execution patterns, and metadata attributes extracted from Solidity source code. A dataset containing 691 Ethereum smart contracts was utilized, consisting of 358 non-vulnerable and 333 vulnerable contracts generated through rule-assisted vulnerability annotation. Ensemble learning models including Random Forest, XGBoost, and LightGBM were evaluated to classify vulnerable and non-vulnerable smart contracts. Experimental results demonstrate that the proposed framework achieved an accuracy of 98.56%, precision of 98.51%, recall of 98.51%, and F1-score of 98.51%. In addition, SHAP explainability analysis revealed that execution-related Solidity operations such as success, gas, call, and staticcall significantly influence vulnerability prediction outcomes. Compared with previous CNN-based, LSTM-based, and opcode-based approaches, the proposed framework achieved superior classification performance while additionally providing interpretable security analysis. The findings indicate that hybrid feature engineering combined with explainable ensemble learning provides an effective and lightweight solution for Ethereum smart contract vulnerability detection and blockchain security analysis.
Explainable Artificial Intelligence Framework for Blockchain Transaction Risk Prediction Using Behavioral Indicators and Transaction Patterns Maram Merlam Allem
Journal of Current Research in Blockchain Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i3.77

Abstract

Blockchain technology has become a fundamental infrastructure for decentralized financial systems and digital asset transactions. However, the increasing volume and complexity of blockchain transactions have introduced significant security challenges, including the identification of potentially risky or suspicious activities. This study proposes an Explainable Artificial Intelligence (XAI) framework for blockchain transaction risk prediction using behavioral indicators and transaction patterns. The proposed framework integrates Extreme Gradient Boosting (XGBoost) for classification, Synthetic Minority Oversampling Technique (SMOTE) for addressing class imbalance, and SHapley Additive exPlanations (SHAP) for model interpretability. The experiments were conducted on a blockchain transaction risk dataset containing 9,000 transaction records with behavioral features such as transaction amount, transaction fee, wallet age, transaction velocity, mixing service usage, cross-border activity, and darknet association indicators. The results show that SMOTE effectively balanced the dataset and improved the detection of minority-class transactions. The proposed model achieved an accuracy of 68.17%, precision of 18.45%, recall of 30.82%, F1-score of 23.09%, and ROC-AUC of 52.67%. Although the predictive performance remains limited, the explainability analysis provides valuable insights into the factors influencing transaction risk. Feature importance analysis identified mixing_service_used and cross_border_flag as the most influential variables, while SHAP analysis highlighted unusual_pattern_score, avg_wallet_transaction_value, and total_wallet_transactions as the most impactful predictors. The findings demonstrate the usefulness of explainable artificial intelligence for understanding blockchain transaction risk and identifying key behavioral indicators associated with suspicious activities. The proposed framework contributes to blockchain security research by enhancing transparency in machine learning-based risk assessment and providing insights for future development of intelligent blockchain monitoring systems.
Adaptive Blockchain-Orchestrated Supply Chain Resilience Through Predictive Maritime Disruption Intelligence Rashad J. Rashes
Journal of Current Research in Blockchain Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i3.80

Abstract

Global maritime supply chains have become increasingly vulnerable to systemic disruptions caused by pandemics, geopolitical conflicts, port congestion, and logistics instability. These disruptions significantly affect freight volatility, operational efficiency, and global trade resilience. This study proposes an Adaptive Blockchain-Orchestrated Supply Chain Resilience Framework using predictive maritime disruption intelligence to enhance adaptive logistics coordination and proactive risk mitigation. The proposed framework integrates maritime congestion analytics, supply chain vulnerability indicators, machine learning-based predictive intelligence, and blockchain-enabled adaptive orchestration into a unified resilience architecture. An XGBoost predictive model was developed using multi-dimensional maritime disruption features, including congestion index, supply chain pressure index, operational delays, trade flow indicators, and vulnerability metrics. Experimental results demonstrated strong predictive performance, with the proposed model achieving an R² value of 0.842 in forecasting maritime shipping rate volatility. Feature importance analysis revealed that systemic supply chain pressure was the dominant predictor of freight disruption dynamics. The adaptive risk evaluation further identified significant disruption escalation during the 2020–2022 global logistics crisis. In addition, the blockchain orchestration layer generated dynamic resilience responses, including route optimization, supplier reallocation, and smart contract activation for decentralized operational coordination. The findings indicate that predictive blockchain-enabled maritime intelligence can substantially improve supply chain resilience through real-time disruption prediction, adaptive decision-making, and automated operational response generation. This research contributes to the development of intelligent resilient logistics systems for future global maritime supply chain management.
Explainable Machine Learning for Smart Contract Vulnerability Detection in Ethereum Blockchain SR Vasantakumaren
Journal of Current Research in Blockchain Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i3.81

Abstract

Smart contracts play an important role in Ethereum blockchain systems by enabling decentralized and automated transactions without intermediaries. However, vulnerabilities in Solidity smart contracts remain a major security issue because attackers can exploit coding weaknesses such as reentrancy attacks, unsafe external calls, and insecure access control mechanisms. Traditional vulnerability detection methods, including manual auditing and static analysis, often require high expertise and lack interpretability. Therefore, this study proposes an Explainable Machine Learning framework for smart contract vulnerability detection using Solidity source code analysis. The proposed framework integrates TF-IDF feature extraction, XGBoost classification, and SHAP (SHapley Additive Explanations) to provide high detection performance and transparent model interpretability. A dataset consisting of 691 Ethereum smart contracts, including 346 safe contracts and 345 vulnerable contracts, was used in this study. The experimental results show that the proposed model achieved an accuracy of 97.84%, with precision, recall, and F1-score values above 97%. The ROC analysis produced an AUC score of 0.9946, indicating excellent classification capability between vulnerable and safe smart contracts. The SHAP analysis revealed that features such as success, staticcall, gas, and origin significantly influenced vulnerability prediction, while security mitigation features such as nonreentrant and reentrancyguard were also identified as important indicators. The results demonstrate that the proposed framework provides accurate, interpretable, and transparent vulnerability detection for Ethereum smart contracts and can assist blockchain developers and security auditors in improving blockchain security analysis.

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