Fraud Detection and Prevention in Blockchain Systems Using Machine Learning
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Abstract
Fraudulent activities pose a significant challenge in the realm of blockchain systems, undermining their inherent trust and security. This research explores the integration of machine learning techniques for enhancing fraud detection and prevention within blockchain networks. By leveraging the immutable nature of blockchain and the predictive capabilities of machine learning algorithms, this study aims to develop a robust framework for detecting and mitigating fraudulent behaviors in decentralized systems.
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References
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