Hybrid Generative Intelligence for Crypto Security and Forecasting: A Java-Based Cloud-Native Framework
DOI:
https://doi.org/10.15680/IJCTECE.2024.0704016Keywords:
Hybrid Generative Intelligence, Cryptocurrency Security, Fraud Detection, Volatility Forecasting, Cloud-Native Architecture, Java Microservices, Deep Learning, Graph Neural Networks, Transformer Models, Blockchain AnalyticsAbstract
The rapid evolution of cryptocurrency ecosystems has introduced significant challenges in ensuring transactional security and accurately forecasting market volatility. This paper proposes a hybrid generative intelligence framework that integrates advanced generative artificial intelligence techniques with cloud-native, Java-based architectures to address these challenges. The framework combines transformer-based models, graph neural networks, and probabilistic generative models to detect fraudulent activities and predict cryptocurrency price volatility with enhanced accuracy. By leveraging both on-chain transaction data and off-chain sources such as market indicators and social sentiment, the system provides a comprehensive analytical approach to understanding complex blockchain dynamics.
The adoption of a Java-based cloud-native framework enables scalability, resilience, and real-time processing capabilities through microservices, containerization, and distributed computing. The hybrid approach enhances model adaptability and robustness by incorporating synthetic data generation and multimodal learning strategies. Experimental findings suggest that the proposed system significantly improves fraud detection rates and forecasting precision compared to traditional methods.
Despite its advantages, the framework faces challenges related to computational complexity, interpretability, and data privacy. This research highlights the transformative potential of hybrid generative intelligence in cryptocurrency analytics while emphasizing the need for continued innovation in explainability, efficiency, and secure deployment strategies.
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