This rule employs machine learning to detect suspicious Windows processes that have been spawned by a parent process exhibiting unusually high scores for malicious probability. It identifies clusters of these processes, predicted to be malicious by the ProblemChild ML model. The detection focuses on instances where multiple processes share the same parent process name, and the aggregate malicious score of this cluster is notably elevated. This method is effective in exposing steganographic attacks where attackers exploit legitimate binaries (LOLBins) to avoid detection by conventional rules. The rule is particularly notable for its application to living-off-the-land attack scenarios, relying on comprehensive data from Windows process events. Upon identification, it recommends specific investigation and remediation steps to effectively handle potential threats.