This rule detects suspicious clusters of Windows processes that may exhibit malicious behavior based on machine learning analysis. It identifies abnormal scores for malicious probability through the ProblemChild supervised ML model, while also assessing clusters of processes from the same host with unusually high aggregate scores using an unsupervised ML approach. The detection focuses on 'living-off-the-land' activities, where legitimate tools (known as LOLbins) are exploited to execute potentially harmful actions without raising alarms. The rule requires the integration of Living off the Land Attack Detection assets and the collection of Windows process events via Elastic solutions like Elastic Defend and Winlogbeat. It emphasizes the need for a comprehensive analysis of suspicious processes, including investigating patterns, correlating related security events, and assessing network activity for additional indicators of compromise. Mitigating false positives from legitimate administrative tools and ensuring refined monitoring based on operational contexts is critical for effective detection.