Anomaly Detection for Cybersecurity and the Need for Explainable AI

Machine learning is increasingly applied in the cybersecurity domain in order to build solutions capable of protecting against attacks that escape rule-based systems. Attacks are nowadays constantly evolving, since adversaries are always creating new approaches or tweaking existing ones: it is thus not possible to rely exclusively on supervised techniques. This talk will focus on the role of anomaly detection techniques in real-world security applications, and how explainability is necessary in order to translate the anomalies detected by the system into actionable events.