The rule titled 'Unusual Windows Remote User' leverages machine learning to detect irregularities in remote desktop protocol (RDP) logins on Windows systems. Specifically, it identifies the use of uncommon usernames, which may signal potential security threats such as account takeovers and credential misuse. Given the rising prevalence of RDP-based attacks, including notable threats like BlueKeep, monitoring such anomalies is crucial for identifying possible unauthorized access. The rule functions by analyzing user login patterns over a 15-minute interval, deriving insights from collected machine learning data related to Windows RDP usage. While its sensitivity threshold is set to capture significant deviations (anomaly threshold of 50), it also allows for consideration of false positives, such as legitimate uncommon usage by engineers for troubleshooting tasks. The overall risk score assigned to this detection is low (21), indicating that while the rule is pertinent, the immediate threat level is manageable for security teams. The successful implementation involves integrating with Elastic Security and ensuring proper ML job setups to detect and analyze these anomalies effectively.