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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. 1 (2025): Regular Issue March" : 5 Documents clear
Comparative Analysis of LightGBM and XGBoost for Predictive Risk Assessment in Blockchain Transactions within the Metaverse Srinivasan, Bhavana
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i1.23

Abstract

The growing integration of blockchain technology within the metaverse has created an urgent need for effective systems to assess and mitigate transaction risks. This study investigates the use of machine learning models, specifically LightGBM and XGBoost, for predictive risk analysis in blockchain transactions. A dataset comprising 50,000 blockchain transactions, with 75% categorized as low-risk and 25% as high-risk, was used to evaluate the performance of these models across key metrics. LightGBM emerged as the superior model, achieving an accuracy of 91.2%, surpassing XGBoost's 89.5%. Additionally, LightGBM recorded an AUC-ROC score of 0.94, outperforming XGBoost’s 0.92. In terms of computational efficiency, LightGBM demonstrated clear advantages. It required only 80 seconds for training and 10 milliseconds per prediction, whereas XGBoost needed 120 seconds for training and 15 milliseconds for prediction. Feature importance analysis further highlighted the pivotal role of the Risk Score, which contributed 40% and 35% to the predictive power of LightGBM and XGBoost, respectively. Other significant features included Amount (USD) and Session Duration, showcasing the relevance of both behavioral and transactional data in risk prediction. These results underscore LightGBM's suitability for real-time risk assessment, making it a reliable and efficient tool for managing large transaction volumes in blockchain ecosystems. However, this study also acknowledges some limitations, including the imbalanced dataset and the static nature of the models, which may struggle with evolving transaction patterns. Future research could address these challenges by employing advanced resampling techniques to balance the dataset, incorporating additional contextual features, and developing adaptive models capable of handling dynamic risk profiles. Through these advancements, this research contributes to the foundation for scalable and secure risk assessment systems, fostering trust in blockchain-based metaverse applications.
Discovering Co-Occurrence Patterns Among Blockchain Address Categories Using the FP-Growth Association Mining Algorithm Lenus, Latasha
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i1.24

Abstract

This paper focuses on identifying recurring patterns among blockchain address categories using the FP-Growth algorithm, which is known for its efficiency in mining frequent itemsets within large datasets. The study provides insights into blockchain ecosystem dynamics by analyzing category associations across different blockchain networks like Ethereum and Bitcoin. Through this analysis, significant patterns were found, such as the frequent co-occurrence of categories related to smart contracts and exchanges, highlighting the central role of these categories in blockchain interactions. Additionally, the study delves into the influence of data sources on detected patterns, revealing that various data collection methods contribute to distinct biases, which affect category associations. The findings offer practical applications for blockchain analytics, such as improving classification models, anomaly detection, and enhancing regulatory compliance. This study contributes to blockchain research by showcasing how association rule mining can improve the categorization and understanding of blockchain address behaviors. The use of FP-Growth, as opposed to more traditional methods, enables faster and more comprehensive analysis, which is particularly valuable given the extensive nature of blockchain datasets. The research also points to potential directions for future work, such as integrating temporal data to observe changes over time and exploring additional blockchain networks to broaden the scope of insights. The study emphasizes the need for continuous advancements in blockchain address analysis to support security, transparency, and regulatory initiatives within this rapidly evolving digital ecosystem.
A Comprehensive Study on Public and Private Blockchain Performance Oh, Lee Kyung; Sukmana, Husni Teja
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i1.25

Abstract

Blockchain technology has emerged as a transformative innovation, with applications spanning diverse industries. This study provides a comprehensive comparison between public and private blockchains, focusing on six key dimensions: scalability, security, use case distribution, energy efficiency, developer ecosystem, and performance metrics. Data were collected from 30 blockchain systems, representing a wide range of consensus mechanisms and industry applications. The findings reveal significant trade-offs between the two blockchain types. Public blockchains, such as Bitcoin and Ethereum, excel in decentralization and transparency, making them ideal for open and trustless environments like cryptocurrency and decentralized finance (DeFi). However, they face limitations in scalability, high energy consumption, and slower transaction speeds. Conversely, private blockchains, such as Hyperledger Fabric and Corda, demonstrate superior scalability, energy efficiency, and privacy, making them more suitable for controlled environments like healthcare, supply chain management, and enterprise financial services. The study underscores the importance of aligning blockchain technology selection with specific application requirements. Furthermore, it highlights the potential of hybrid blockchain models to integrate the strengths of both public and private systems, addressing existing limitations. These findings provide valuable insights for organizations and developers in leveraging blockchain technologies effectively.
Cyber Attack Pattern Analysis Based on Geo-location and Time: A Case Study of Firewall and IDS/IPS Logs Mashao, Daniel; Harley, Charis
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i1.26

Abstract

Cyber attacks are a growing concern for organizations worldwide, requiring continuous monitoring and analysis to detect patterns and anticipate future threats. This study explores the temporal and geographical patterns of cyber attacks using log data from firewall and IDS/IPS systems, with a focus on understanding attack trends based on severity levels and monthly variations. The analysis revealed an almost even distribution of attacks, with 13,183 low severity, 13,435 medium severity, and 13,382 high severity incidents. This emphasizes the need for holistic defense strategies that address all levels of threats. Through time-series analysis, including the ARIMA model, we forecasted future attack trends, highlighting the consistency of cyber threats over time and identifying potential periods of increased activity. The monthly trend analysis showed fluctuations, with a notable peak of 906 attacks in March 2020 and a decrease to 825 attacks in April 2020, suggesting the influence of external factors such as global events. The ARIMA model provided accurate forecasts, indicating a steady rate of future attacks and underscoring the importance of continuous vigilance. While the ARIMA model captured linear trends effectively, future work should explore non-linear models, such as Long Short-Term Memory (LSTM) networks, to uncover deeper, more complex patterns in the data. This research provides critical insights into the nature of cyber attacks, offering organizations a data-driven approach to improving their cybersecurity measures. Future studies should focus on enhancing forecasting models and integrating real-time data to better anticipate emerging threats.
Predicting Throughput and Latency in Hyperledger Fabric Blockchains Using Random Forest Regression Dewi, Deshinta Arrova; Kurniawan, Tri Basuki
Journal of Current Research in Blockchain Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i1.27

Abstract

The study focuses on enhancing the performance optimization of Hyperledger Fabric blockchains through predictive modeling using Random Forest regression. It emphasizes the importance of accurately predicting two critical performance metrics—throughput (measured in transactions per second or TPS) and latency (defined as the time taken to confirm transactions). These metrics directly influence the efficiency and user experience of blockchain applications, making their accurate prediction essential for configuring blockchain networks effectively. The research leverages data collected through Hyperledger Caliper, a benchmarking tool, which provides detailed measurements of various configuration parameters, including block size, transaction arrival rate, and the number of orderer nodes. Through rigorous exploratory data analysis, the study identifies how these parameters impact throughput and latency, revealing complex interdependencies that challenge traditional optimization approaches. Using Random Forest regression, a robust ensemble learning method, the study demonstrates that the predictive model can achieve high accuracy. The performance of the model is assessed using metrics such as R-squared values, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), which collectively underscore its ability to offer reliable predictions across varying configurations. The results of this research provide practical insights for blockchain administrators, allowing them to configure Hyperledger Fabric settings more efficiently, thereby reducing the trial-and-error process typically involved in performance tuning. Moreover, the study's findings contribute to the broader field of blockchain performance optimization by offering a data-driven framework that bridges theoretical analysis with practical application in real-world scenarios. Looking forward, the study suggests avenues for future research, including expanding the dataset to cover more diverse blockchain platforms and configurations, incorporating real-world deployment data for validation, and exploring additional machine learning algorithms for even greater predictive accuracy. This approach highlights the critical role of data-driven methodologies in optimizing blockchain network performance and encourages further collaboration and exploration in the domain.

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