Fuzz testing of machine learning models to detect malicious activity on a computer

Abstract

An example method can include, obtaining information about a machine learning (ML) model configured to detect malicious activity on a computer system, wherein the information includes one or more of a model type of the ML model, an output type of the ML model, or a type of malicious activity that the model is trained to detect, receiving a training dataset, wherein the training dataset includes a plurality of unlabeled examples, generating an additional dataset based on the training dataset using a generative model, wherein the additional dataset includes a plurality of additional unlabeled examples, and, training the machine learning model to generate labels for each example in the training dataset and the additional dataset, using a combination of the training dataset and the additional dataset, wherein the training includes adjusting one or more parameters of the machine learning model based on accuracy of the generated labels.