The 'Unusual Login Activity' detection rule identifies an unusually high number of authentication attempts using a machine learning approach. It is designed to detect anomalies in login patterns that may indicate brute force attacks or unauthorized access attempts. The rule is based on a machine learning model identified by the job ID 'suspicious_login_activity', which analyses login frequency and patterns over a configurable time window. If the number of login attempts exceeds the established anomaly threshold of 50 within a specified interval of 15 minutes, alerts are triggered. This contributes to enhancing threat detection for credential abuse and auditing identity access. The rule requires integration with the Elastic security ecosystem, specifically through Elastic Defend, Auditd Manager, or System integrations. Given the complexity of login behaviors, proper triage and investigation steps are crucial for interpreting alerts accurately, especially to identify potential false positives stemming from legitimate usage patterns or automated scripts. If alerted, security teams are guided through various investigation methodologies such as reviewing source IP addresses and correlating with other security events to assess the legitimacy of the alerts.