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Strategies to Improve Detection Accuracy of Graph Neural Networks Against Sybil Attacks in Decentralized Identity Systems
This research proposes strategies to enhance the detection accuracy of graph neural networks in combating Sybil attacks within decentralized identity systems. By optimizing network architectures and training methodologies, we improve attack resilience, ensuring more secure and reliable identity verification processes in decentralized environments.
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