1. A method comprising: constructing a first collection, the first collection comprising a first feature vector and a Q&A feature vector; constructing a second collection from the first collection by inserting noise data in at least one of the first feature vector and the Q&A feature vector, wherein the noise is inserted by changing an existing value in the first feature vector by a random amount; further constructing a third collection by using at least one of (i) combining, to crossover, at least one of a first feature vector and a Q&A feature vector of the second collection with a corresponding at least one of a first feature vector and a Q&A feature vector of a fourth collection, wherein the second and the fourth collections have a property similar to one another, and (ii) combining, to migrate, at least one of a first feature vector and a Q&A feature vector of the second collection with a corresponding at least one of a first feature vector and a Q&A feature vector of a fifth collection, wherein the second and the fifth collections have a property distinct from one another; aging, using a forecasting configuration, a first feature vector of the third collection to generate a changed feature vector, the changed feature vector containing feature values expected at a future time; predicting, by inputting the changed feature vector in a trained neural network, a probability of the cyber-attack occurring at the future time.