User and Entity Behavior Analytics (UEBA) systems are increasingly used to identify suspicious activities and cybersecurity threats in complex infrastructures. This work presents a deep-learning-based anomaly-detection framework leveraging deep autoencoders to model behavioral patterns and identify anomalous activities within cybersecurity environments.
The proposed methodology focuses on scalable and adaptive threat-detection mechanisms capable of supporting next-generation Security Operations Centers through explainable and data-driven cybersecurity analytics.