AI-Driven Blockchain Analytics: Leveraging Deep Learning for Fraud Detection in Decentralized Networks
Keywords:
AI, blockchainAbstract
The emergence of blockchain technology has revolutionized various industries by enabling decentralized transactions with enhanced security and transparency. However, the increasing complexity of blockchain data also opens avenues for fraudulent activities, posing significant challenges to the integrity of decentralized networks. This research explores the application of artificial intelligence (AI), particularly deep learning techniques, in analyzing blockchain data for the effective detection of fraudulent activities. By leveraging advanced algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), this study aims to improve the accuracy of fraud detection systems while minimizing false positives. Additionally, the paper discusses the architecture of deep learning models tailored for blockchain analytics, the challenges associated with data privacy and model training, and potential solutions to enhance the robustness of these systems. Through a comprehensive review of existing literature and real-world case studies, this research highlights the transformative potential of AI-driven blockchain analytics in safeguarding decentralized networks against fraud.
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