An Anomaly Detection Scheme for Blockchain Transactions Based on AdaBoost Model
In response to potential anomalous behaviors,such as coin theft in the transaction records of the blockchain-based cryptocurrency,a detection scheme with privacy protection function based on the adaptive boosting(AdaBoost)model was proposed.This scheme integrated additive homomorphic encryption and matrix perturbation techniques,ensuring the preservation of transaction data privacy while effectively identifying and predicting anomalies.The scheme's underlying protocol was designed and implemented in a cloud outsourcing environment,and its correctness and privacy protection properties were proven.Compared with similar protocols,this scheme has high detection accuracy and recall while ensuring privacy.The detection time for each record was at the millisecond level,making it suitable for real cryptocurrency transaction detection scenarios.
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维普
