Methods and apparatus for visualization of machine learning malware detection models

Abstract

Embodiments disclosed include methods and apparatus for visualization of data and models (e.g., machine learning models) used to monitor and/or detect malware to ensure data integrity and/or to prevent or detect potential attacks. Embodiments disclosed include receiving information associated with artifacts scored by one or more sources of classification (e.g., models, databases, repositories). The method includes receiving inputs indicating threshold values or criteria associated with a classification of maliciousness of an artifact and for selecting sample artifacts. The method further includes classifying and selecting the artifacts, based on the criteria, to define a sample set, and based on the sample set, generating a ground truth indication of classification of maliciousness for each sample artifact in the sample set. The method further includes using the ground truth indications to evaluate and display, via an interface, a representation of a performance of sources of classification and/or quality of data.