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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. 2 No. 3 (2025): Regular Issue September 2025" : 5 Documents clear
A Study of Known Vulnerabilities and Exploit Patterns in Blockchain Smart Contracts Astriratma, Ria
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.40

Abstract

Blockchain smart contracts are pivotal to decentralized applications, yet their security remains a critical challenge. This study analyzes a dataset of 1,000 smart contracts to investigate known vulnerabilities, audit practices, and exploit patterns. The results reveal that audited contracts are significantly less prone to exploitation, with 75% exhibiting no exploit history compared to 55% of non-audited contracts. "Integer Overflow" and "Unchecked Call" were identified as the most prevalent vulnerabilities, contributing to 60% and 50% exploit rates, respectively. The study highlights the importance of transparent audit reporting, as contracts without available reports were exploited in 35% of cases. Additionally, hidden vulnerabilities in ostensibly secure contracts underscore the evolving sophistication of blockchain threats. This research emphasizes the need for robust security practices, including stricter coding standards, comprehensive audits, and advanced vulnerability detection techniques such as formal verification and machine learning. Future works aim to integrate security tools into development workflows and foster industry-wide collaboration to standardize auditing practices, thereby enhancing the security and trustworthiness of blockchain ecosystems.
Analysis of Gas Fee Patterns in Blockchain Transactions - A Case Study on Ethereum Smart Contracts Paramitha, Adi Suryaputra; Tarigan, Masmur
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.41

Abstract

Gas fees play a crucial role in Ethereum blockchain transactions, directly affecting the cost and efficiency of decentralized applications. This study analyzes gas fee patterns across transaction types, temporal trends, and anomalous behaviors using a dataset of 1,000 Ethereum transactions. The results reveal that the average gas price was 120.5 Gwei, with a standard deviation of 45.2 Gwei, highlighting significant variability. Smart contract functions exhibited varying gas usage, with mint operations consuming the highest average gas (1,500,000 units) compared to approve (1,200,000 units) and transfer (800,000 units). A positive correlation (r = 0.65) was observed between gas price and value transferred, suggesting that higher-value transactions often incur elevated gas fees. Temporal analysis showed predictable patterns, with peak gas prices occurring between 13:00 - 17:00 UTC during high network activity and lower prices between 02:00 - 06:00 UTC. Additionally, anomaly detection identified 15 outlier transactions, including one with an unusually high gas price of 500 Gwei, reflecting network congestion or prioritization strategies. These findings provide actionable insights for optimizing transaction costs and improving smart contract efficiency. Future research could explore layer-2 scaling solutions, alternative fee mechanisms, and machine learning approaches for gas price prediction. This study contributes to a deeper understanding of Ethereum’s gas fee dynamics, offering valuable guidance for developers, users, and researchers in the blockchain ecosystem.
Blockchain Node Classification Predicting Node Behavior Using Machine Learning Prasetio, Agung Budi; Purbo, Ono Widodo
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.42

Abstract

Blockchain technology has emerged as a secure and decentralized framework for digital transactions; however, its open and pseudonymous nature also presents significant challenges related to fraudulent activities and malicious nodes. This study investigates the application of machine learning models for blockchain node classification and fraud detection, evaluating three models: Random Forest, XGBoost, and Neural Network. The research leverages a dataset of 10,000 blockchain transactions with 16 attributes, including transaction fees, block scores, stake distribution rates, and coinage. The results demonstrate that machine learning models can effectively classify blockchain nodes with high accuracy. Among the evaluated models, the Neural Network classifier outperformed the others, achieving an accuracy of 95.3%, precision of 95.1%, recall of 95.6%, and an F1-score of 95.3%. Comparatively, XGBoost achieved an accuracy of 94.1%, while Random Forest scored 92.4%. Feature importance analysis highlighted Block Score (0.38), Transaction Fee (ETH) (0.30), and Stake Distribution Rate (0.15) as the most significant factors influencing classification outcomes. Furthermore, confusion matrix analysis revealed that the Neural Network model produced 4780 true positives and 4440 true negatives, with only 200 false positives and 580 false negatives, demonstrating its robustness in identifying fraudulent nodes. Despite these promising results, real-world deployment presents several challenges, including the evolving nature of fraudulent strategies, real-time detection requirements, and scalability concerns. Future research should explore real-time learning techniques, integration of network-based features, decentralized fraud detection mechanisms, and cross-chain anomaly detection to improve model adaptability and effectiveness. By advancing these methods, machine learning-driven fraud detection can contribute to a safer, more transparent, and resilient blockchain ecosystem.
Investigating the Relationship Between Gas Consumption and Value Transferred in Ethereum Contracts Chantanasut, Suraphan
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.43

Abstract

This study investigates the relationship between gas consumption and value transferred in Ethereum smart contracts, offering insights into resource utilization and efficiency within the blockchain ecosystem. Analyzing a dataset of 1,000 smart contracts, a moderate positive correlation r=0.45,p<0.05 was observed, indicating that higher gas consumption generally corresponds to larger financial transactions. The average gas consumption per contract was found to be 58,451,329.47 units, with a standard deviation of 20,123,456.89, highlighting significant variability in computational resource usage. Similarly, the average value transferred was 7,851.47 ETH, ranging from 0.001 ETH to over 100,000 ETH, showcasing the diverse financial applications of smart contracts. Efficiency analysis, measured as the ratio of value transferred to gas consumed, revealed an average efficiency of 0.00013 ETH per unit of gas, with some contracts achieving up to 0.01 ETH per unit of gas and others as low as 0.000007 ETH per unit of gas, reflecting varying levels of optimization. Outliers with disproportionately high gas consumption relative to value transferred were identified, suggesting inefficiencies or unique use cases. These findings underscore the importance of optimizing smart contract design to minimize gas costs and improve performance. Future research directions include functionality-specific analyses, anomaly detection, comparative studies across blockchain platforms, and exploring the economic implications of gas consumption. This work provides actionable insights for developers, researchers, and policymakers aiming to enhance the efficiency and sustainability of decentralized systems.
Stake-Based Block Generation and Its Impact on Ethereum Transaction Efficiency Haodic, Gao; Xing, Zhan
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.44

Abstract

Ethereum's transition from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism has significantly altered the network’s block generation process and transaction efficiency. This study investigates the impact of stake-based block generation on Ethereum’s transaction fees, block density, and overall network performance by analyzing a dataset containing 303 records of Ethereum blockchain activity. The findings reveal a strong positive correlation between block generation rate and stake reward (r = 0.78, p < 0.01) and coin stake (r = 0.74, p < 0.01), indicating that validators with larger stakes generate blocks more frequently. Additionally, transaction fees positively correlate with block density (r = 0.65, p < 0.01), suggesting that network congestion remains a key determinant of transaction costs, despite the PoS transition. Further analysis shows that Ethereum’s PoS system optimizes block space utilization, with an observed mean block density of 1393.6% and a transaction fee standard deviation of 0.12 ETH, demonstrating a more stable fee structure than PoW. The average transaction fee recorded is 0.179 ETH, with a maximum observed fee of 0.98 ETH and a minimum of 0 ETH in some cases. While PoS provides greater fee stability, minor fluctuations in fees persist due to congestion-related effects. Additionally, the mean stake reward is 0.98, suggesting a relatively stable staking incentive structure across different blocks.

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