Abstract: A method includes acquiring blockchain data that includes transactions between a plurality of blockchain addresses. The method includes labeling a set of the blockchain addresses as fraudulent and generating a graph data structure based on the blockchain data. The method includes calculating a set of scoring features for each blockchain address, where each set of scoring features includes a graph-based scoring feature. Calculating the graph-based scoring feature includes calculating a number of transactions associated with the blockchain address in the graph data structure. The method includes generating a scoring model using sets of scoring features for the blockchain addresses that are labeled as fraudulent and generating a trust score for each blockchain address using the scoring features and the scoring model. The trust score indicates a likelihood that the blockchain address is involved in fraudulent activity. Additionally, the method includes sending a requested trust score to a requesting device.

Abstract: A method includes acquiring, at a node server, blockchain data for a blockchain address on a blockchain network. The blockchain data includes a plurality of transactions for the blockchain address. The method includes generating a local node trust score for the blockchain address based on the blockchain data. The local node trust score indicates a likelihood that the blockchain address is involved in fraudulent activity. The method includes receiving, from a plurality of remote servers, a plurality of additional local trust scores for the blockchain address. The method includes determining a consensus trust score based on the local node trust score and the plurality of additional local trust scores. Additionally, the method includes monitoring a ledger on the blockchain network for a contract trust request that specifies the blockchain address and sending, to the blockchain network, the consensus trust score for the blockchain address specified in the contract trust request.

Abstract: A method includes acquiring, at a node server, blockchain data for a blockchain address on a blockchain network. The blockchain data includes a plurality of transactions for the blockchain address. The method includes generating a local node trust score for the blockchain address based on the blockchain data. The local node trust score indicates a likelihood that the blockchain address is involved in fraudulent activity. The method includes receiving, from a plurality of remote servers, a plurality of additional local trust scores for the blockchain address. The method includes determining a consensus trust score based on the local node trust score and the plurality of additional local trust scores. Additionally, the method includes receiving a trust request for the blockchain address from a requesting device and sending the consensus trust score for the specified blockchain address to the requesting device.

Abstract: A method includes acquiring blockchain data that includes transactions between a plurality of blockchain addresses. The method includes labeling a set of the blockchain addresses as fraudulent and generating a graph data structure based on the blockchain data. The method includes calculating a set of scoring features for each blockchain address, where each set of scoring features includes a graph-based scoring feature. Calculating the graph-based scoring feature includes calculating a number of transactions associated with the blockchain address in the graph data structure. The method includes generating a scoring model using sets of scoring features for the blockchain addresses that are labeled as fraudulent and generating a trust score for each blockchain address using the scoring features and the scoring model. The trust score indicates a likelihood that the blockchain address is involved in fraudulent activity. Additionally, the method includes sending a requested trust score to a requesting device.