When preparing automated learning procedures, we will often face a series of challenges. We need to overcome these challenges in order to recognize and avoid compromising the reliability of the procedures themselves, thus preventing the possibility of drawing erroneous or hasty conclusions that, in the context of cybersecurity, can have devastating consequences.
One of the main problems that we often face, especially in the case of the configuration of threat detection procedures, is the management of false positives; that is, cases detected by the algorithm and classified as potential threats, which in reality are not. We will discuss false positives and ML evaluation metrics in more depth in Chapter 7, Fraud Prevention with Cloud AI Solutions, and Chapter 9, Evaluating Algorithms.
The management of false positives is particularly burdensome...