Using Deep Generative Networks for Data Analysis and Quality Enhancement in Blockchain Networks | ||||
| التجارة والتمويل | ||||
| Volume 45, Issue 1, March 2025, Page 152-184 PDF (917.07 K) | ||||
| DOI: 10.21608/caf.2025.422190 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Hatem Sayed Ahmed Abdel Qadear1; Amr Ibrahim Awd El-Shora2 | ||||
| 1Professor of Information Systems and Dean of the Faculty of Computers and Information, Menoufia University, Egypt | ||||
| 2Assistant Professor of Computer Science and Information Systems, currently serving as the Acting Head of the Computer Science Department at the Higher Institute of Management and Information Technology in Kafr El-Sheikh, Egypt. | ||||
| Abstract | ||||
| This study aims to enhance blockchain data integrity and analytical reliability by integrating Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). High-quality data is crucial for improving predictive accuracy, fraud detection, and transaction security. This research develops a deep learning framework that effectively mitigates noise, inconsistency, and redundancy in blockchain data, ensuring more accurate and trustworthy analytics. The proposed methodology follows three key stages: (1) preprocessing blockchain data by identifying and eliminating noise, detecting inconsistencies, and filtering unreliable records to ensure integrity; (2) leveraging GANs and VAEs to generate high-fidelity synthetic data that aligns closely with real-world distributions, reducing biases and improving model robustness; and (3) evaluating the proposed approach using rigorous performance metrics, including precision, recall for fraud detection, and the AUC-ROC curve. The evaluation process involves benchmarking against traditional statistical and rule-based techniques to validate improvements. Experimental findings demonstrate that employing GANs and VAEs enhances analytical model accuracy by 5–7% compared to conventional methods. This improvement was observed across multiple blockchain datasets, indicating strong generalization capabilities. Additionally, integrating AI-driven techniques with blockchain analytics significantly strengthens fraud detection mechanisms and minimizes forecasting errors in financial transactions. To optimize this approach further, the study suggests refining deep generative models to improve computational efficiency, particularly in handling large-scale blockchain networks with real-time constraints. Additionally, reinforcement learning could be explored to enable adaptive data refinement, allowing the model to dynamically adjust to evolving blockchain patterns. Expanding AI applications in fraud detection and transaction monitoring will be vital for securing blockchain-based financial systems. In conclusion, this research highlights the transformative impact of deep generative models on blockchain data analysis. By improving data quality, increasing predictive accuracy, and mitigating fraud risks, this approach contributes to the development of more reliable and scalable blockchain ecosystems. Future research could explore real-time data adaptation techniques and hybrid AI models to further advance decentralized finance and digital asset management, ensuring sustainable and efficient blockchain applications. | ||||
| Keywords | ||||
| Generative Adversarial Networks (GANs); Variational Autoencoders (VAEs); deep generative models; blockchain data quality; synthetic data generation; fraud detection in blockchain transactions; decentralized blockchain systems; data preprocessing | ||||
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