In the previous chapters, we looked at different artificial intelligence (AI) algorithms, analyzing their application to the different scenarios and their use cases in a cybersecurity context. Now, the time has come to learn how to evaluate these algorithms, starting from the assumption that algorithms are the foundation of data-driven learning models.
We will therefore have to deal with the very nature of the data, which is the basis of the algorithm learning process, which aims to make generalizations in the form of predictions based on the samples received as input in the training phase.
The choice of algorithm will therefore fall on the one that is best for generalizing beyond the training data, thereby obtaining the best predictions when facing new data. In fact, it is relatively simple to identify an algorithm that fits the training...