Detecting cheating using machine learning

Abstract

Some implementations relate to methods, systems, and computer-readable media to detect cheating in a virtual experience. Game information including game state information is obtained. A trained machine learning (ML) model generates, based on the game information, output that characterizes user behaviors in a virtual experience. The output includes cheating analysis data that characterizes the user behaviors by indicating a cheating likelihood for one or more users. The game information may be preprocessed by being flattened and serialized prior to providing it to the ML model. If the cheating analysis data indicates that the user is cheating with a likelihood exceeding a threshold probability, the game server may perform an anti-cheat operation.

Dependencies are inferred from the claim text.