Systems and methods for controlling computing systems associated with network operations

Abstract

Presented herein are systems and methods of training machine learning (ML) models to determine likelihoods of fraud in network operations caused by computing systems. A server may generate training data to include (i) a digital fingerprint associated with an identity of a computing system of a plurality of computing systems and (ii) a plurality of network operation metrics associated with the computing system. The server may label the training data to indicate whether fraudulence is caused by the computing system. The server may execute, using the training data, a ML model having a plurality of weights to generate a likelihood of fraud caused by the computing system. The server may compare the likelihood of fraud with labeled training data to determine an error metric in accordance with a loss function. The server may update at least one of the plurality of weights using the error metric.