Deep learning based method and system for detecting abnormal cryptocurrency transaction between computing devices in a blockchain network

A deep learning based method for detecting abnormal cryptocurrency transactions between computing devices in a blockchain network, the method comprising:

• establishing, by a communication interface of a fraud detection server, a communication link with a plurality of computing devices in the blockchain network,
• the plurality of computing devices comprising a first computing device associated with a first user wallet address and one or more second computing devices associated with one or more second user wallet addresses,
• the fraud detection server comprising a memory configured to store:
• one or more transaction data of transactions between the first user wallet address and at least one of the one or more second user wallet addresses,
• one or more input codes associated with the transactions between the first user wallet address and the at least one of the one or more second user wallet addresses, and
• one or more bytecodes associated with distributing the input code in the blockchain network;
• generating, by a processor of the fraud detection server, a first data set associated with the first user wallet address based at least in part on the transaction data, the first data set comprising a set of normalized statistical features of the transaction data;
• converting, by the processor, the input codes into a second data set associated with the first user wallet address, the second data set comprising a first set of integers;
• converting, by the processor, the bytecodes into a third data set associated with a contract address of the first user wallet address, the third data set comprising a second set of integers;
• running a pre-learned deep learning module of the processor using the first data set to extract a first feature vector;
• running the pre-learned deep learning module of the processor using the second data set to extract a second feature vector;
• running the pre-learned deep learning module of the processor using the third data set to extract a third feature vector;
• converting, by the processor, the first feature vector, the second feature vector, and the third feature vector into an intermediate value;
• comparing, by the processor, the intermediate value to a predetermined reference value stored in the memory;
• and
• determining, by the processor, a fraudulent activity in the transaction between the first wallet address and the at least one of the one or more second user wallet addresses based on a comparison result.