Systems and methods for generating embeddings of network event data to detect fraudulent behavior in networked environments

Abstract

Presented herein are systems and methods of generating embeddings for network events to detect fraudulent activities in networked environments. A service may receive a request to execute a first network operation in a network environment. The service may identify an event dataset associated with the first network operation to be executed. The service may apply the first event dataset to a machine learning (ML) model comprising a plurality of weights. The ML model may be established using training data comprising (i) a first sample event dataset associated with a second network operation and (ii) a second sample event dataset corresponding to a modification of a portion of the first sample event dataset. The service may generate, based on applying the event dataset of the first network operation to the ML model, a plurality of embeddings indicative of fraudulence of the first network operation.