Abstract: Methods and systems are described herein for a malware detection system that detects whether an on-chain program associated with a cryptographic token is malicious based on output of a machine learning model. The malware detection system may retrieve or generate attributes associated with an on-chain program and input those into a plurality of scripts within a malware detection script database to determine whether the on-chain program is malicious. The scripts may be generated based on an output of a machine learning model indicating whether the on-chain program is malicious.

Abstract: In some aspects, a computing system may use time series data and machine learning to determine an efficient time to send data to a blockchain. A machine learning model may use a variety of blockchain related data to predict a network usage costs for different times in the future. The predicted network usage costs may be used to determine when data should be sent for storing on the blockchain to reduce network resource costs for a computing system. For example, based on inputting the blockchain data into the machine learning model, a computing system may generate output indicating network usage costs for a future time period, with each network usage cost corresponding to a timestamp within the future time period. The computing system may determine a minimum network usage cost of the network usage costs and send data to a node in the blockchain network at the corresponding timestamp.