This paper explains the machine-learning and behavioral-analytics models behind Core Threat Detection & Response. Page 1 outlines the challenge of detecting subtle attacker behaviors across large, noisy telemetry streams. The paper details anomaly detection, adversary-signal modeling, supervised learning, and feedback loops using MITRE ATT&CK. It explains correlation methods across endpoints, networks, and identities. Visuals show scoring models, clustering, and signal-to-noise reduction workflows. The system prioritizes high-value alerts, accelerates investigations, and improves accuracy over traditional SIEM rule-based systems.

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