Machine Learning And Artificial Intelligence In Cybersecurity: Hype Versus Reality

When it comes to training AI/ML models, a popular debate is whether "supervised" or "unsupervised" learning should be used. Supervised learning is based on labeled data and features extracted to derive a prediction model. For malware, this means human experts classify each sample in the data set as good or bad, and feature-engineering is performed to determine what attributes of the malware are relevant to the prediction model prior to training. Unsupervised learning gleans patterns and determines structure from data that is not labeled or categorized. Unsupervised learning proponents claim that it is not limited by the boundaries of human classification and remains free from feature-selection bias.