Method And System For Unsupervised Domain Adaptation For Detecting Crypto Fraud

Abstract: A method for identifying fraudulent cryptographic currency transactions using a deep neural network includes: receiving, by a receiver of a processing server, a source dataset, the source dataset including labeled source data associated with a plurality of source features and being associated with a source domain; receiving, by the receiver of the processing server, a target dataset, the target dataset including unlabeled target data associated with a plurality of target features and being associated with a target domain; combining, by a processor of the processing server, at least a subset of the plurality of source features and at least a subset of the plurality of target features into a combined data layer; training, by the processor of the processing server, a deep neural network using a domain adaptation algorithm and the combined data layer to identify a set of final features.

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