A supervised machine learning model (ProblemChild) has identified a suspicious Windows process event with high probability of it being malicious activity. Alternatively, the model's blocklist identified the event as being malicious.

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A machine learning job combination has detected a set of one or more suspicious Windows processes with unusually high scores for malicious probability. These process(es) have been classified as malicious in several ways. The process(es) were predicted to be malicious by the ProblemChild supervised ML model. If the anomaly contains a cluster of suspicious processes, each process has the same host name, and the aggregate score of the event cluster was calculated to be unusually high by an unsupervised ML model. Such a cluster often contains suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules.

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A machine learning job combination has detected a set of one or more suspicious Windows processes with unusually high scores for malicious probability. These process(es) have been classified as malicious in several ways. The process(es) were predicted to be malicious by the ProblemChild supervised ML model. If the anomaly contains a cluster of suspicious processes, each process has the same parent process name, and the aggregate score of the event cluster was calculated to be unusually high by an unsupervised ML model. Such a cluster often contains suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules.

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A machine learning job combination has detected a set of one or more suspicious Windows processes with unusually high scores for malicious probability. These process(es) have been classified as malicious in several ways. The process(es) were predicted to be malicious by the ProblemChild supervised ML model. If the anomaly contains a cluster of suspicious processes, each process has the same user name, and the aggregate score of the event cluster was calculated to be unusually high by an unsupervised ML model. Such a cluster often contains suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules.

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A machine learning job has detected a suspicious Windows process. This process has been classified as suspicious in two ways. It was predicted to be suspicious by the ProblemChild supervised ML model, and it was found to be an unusual process, on a host that does not commonly manifest malicious activity. Such a process may be an instance of suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules.

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A machine learning job has detected a suspicious Windows process. This process has been classified as malicious in two ways. It was predicted to be malicious by the ProblemChild supervised ML model, and it was found to be an unusual child process name, for the parent process, by an unsupervised ML model. Such a process may be an instance of suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules.

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A machine learning job has detected a suspicious Windows process. This process has been classified as malicious in two ways. It was predicted to be malicious by the ProblemChild supervised ML model, and it was found to be suspicious given that its user context is unusual and does not commonly manifest malicious activity,by an unsupervised ML model. Such a process may be an instance of suspicious or malicious activity, possibly involving LOLbins, that may be resistant to detection using conventional search rules.

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A supervised machine learning model (ProblemChild) has identified a suspicious Windows process event with low probability of it being malicious activity. Alternatively, the model's blocklist identified the event as being malicious.

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