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.

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