Cyberattack forecasting using predictive information

Abstract

A computerized method and system for predicting the probability of a cyberattack to a target entity, includes: collecting a plurality of predictive signals to a target entity for a specific cyberattack type; optionally, imputing a value for missing values of the collected signals; selecting a set of relevant non-redundant signals from the collected signals to create lagged signals; identifying from the lagged signals relevant data chunks to form a custom training set of signals; providing selected ground truth data related to the specific attack type for the target entity; training a forecasting model using the custom training set of signals together with the selected ground truth data related to the specific attack type for the target entity to generate a trained forecasting model; providing a second set of signals of the same type of signals as the custom training set of signals; and generating the probability of the specific attack type of interest against the target entity by inputting the second set of signals into the trained forecasting model.