Abstract: Systems and methods for detection of unreported cyber events experienced by an entity of interest include a server, processors, or software employing a machine learning algorithm having been trained on cybersecurity data for a plurality of entities, wherein each entity is a company or an organization. The cybersecurity data is provided by having been transformed into a plurality of images that convey the cybersecurity data for the plurality of entities. The machine learning algorithm is used for generating a predicted number of cyber events experienced by the entity of interest. A reported number of cyber events experienced by the entity of interest is monitored and compared to the predicted number of cyber events experienced by the entity of interest. Based on this comparison, a predicted unreported number of cyber events experienced by the entity of interest is generated.

Abstract: A computer-implemented method for detection of unreported cyber events experienced by an entity of interest is provided. The method includes instructions including obtaining training data related to estimating historical cyber health of a plurality of entities, training a neural network on the training data to create a trained neural network, and utilizing the trained neural network to generate a predicted number of cyber events experienced by the entity of interest during a time period. The instructions further include monitoring a reported number of cyber events experienced by the entity of interest during the period and generating a predicted unreported number of cyber events experienced by the entity of interest during the period based upon comparing the predicted number of cyber events experienced by the entity of interest during the period to the reported number of cyber events experienced by the entity of interest during the period.