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All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Current Research in Blockchain International Journal Research on Metaverse
Guballo, Jayvie Ochona
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Journal : Journal of Current Research in Blockchain

 1 
Cybersecurity and Audit Compliance in Blockchain and Their Implications for System Resilience and Transaction Errors Catamio, Francis G.; Guballo, Jayvie Ochona
Journal of Current Research in Blockchain Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i4.50

Abstract

This study investigates the influence of cybersecurity indicators and audit compliance on transaction reliability and customer trust within blockchain systems. Using a dataset containing daily records of operational and security metrics, the research employs descriptive statistics, correlation analysis, and multiple linear regression to evaluate how key variables—namely security incidents, audit compliance scores, and reported cyberattacks—affect transaction errors and user trust. The analysis reveals that Security Incidents are positively correlated with Transaction Errors per Million (r = 0.64), while Audit Compliance Score (%) shows a negative correlation with transaction errors (r = -0.47) and a positive correlation with Customer Trust Index (r = 0.58). A multiple regression model indicates that approximately 68.3% of the variance in transaction errors is explained by the selected predictors (Adjusted R² = 0.683). Security Incidents are a statistically significant positive predictor (p < 0.01), and Audit Compliance Score (%) is a significant negative predictor (p < 0.05), whereas Cyber Attacks Reported show no statistically significant effect. Visual analyses further confirm these relationships: systems with higher audit compliance scores tend to exhibit fewer errors and greater user trust, while those with frequent security incidents experience higher transactional failures. These findings underscore the importance of integrating both security and audit mechanisms in blockchain risk management frameworks. Future research is recommended to incorporate additional cybersecurity dimensions and explore longitudinal trends across different blockchain architectures.
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.
Co-Authors Andes, Joy April C. Catamio, Francis G.