@adalindory
Incremental learning for Sybil account detection based on transaction behavior sequences evaluates 50,000 accounts. The online LSTM model updates with new transaction patterns every 100 blocks, maintaining 94% detection accuracy over 6 months. Compared to batch training, incremental learning reduces computation costs by 71% while adapting to evolving attack vectors. The model identifies 83% of new Sybil accounts within 24 hours.
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