The "Anomalous Process For a Linux Population" detection rule identifies rare processes that are executed across multiple Linux hosts within a network or fleet of machines. By leveraging machine learning, the rule calibrates to recognize standard operations while minimizing false positives, typically arising from sporadic automated maintenance tasks that usually affect only a single host. The rule analyzes processes run in the last 45 minutes at 15-minute intervals, flagging any unusual activity statistically divergent from established baselines for legitimate user behavior. It integrates with both Elastic Defend and the Auditd Manager, allowing comprehensive monitoring of audit events and system activity to enhance the detection of potential threats. The setup requires pre-installed machine learning jobs and connections with the specified integrations. Adopting this detection capability is vital for organizations aiming to identify potentially malicious behavior disguised as legitimate process execution.