Extrahop Networks, Inc.

Edmund Hope Driggs

Jesse Abraham Rothstein

Xue Jun Wu

Michael Kerber Krause Montague

Benjamin Thomas Higgins

Nicholas Jordan Braun

Jeff James Costlow

Joel Benjamin Deaguero

Jonathan Jacob Scott

Marc Adam Winners

Michael Ryan Corder

Michael Christopher Kelly

Kenneth Perrault

Embodiments are directed to monitoring network traffic using network monitoring computers (NMCs). Metrics may be determined based on monitoring network traffic associated with a plurality of entities each associated with a profile that includes the metrics for each entity. Beaconing metrics associated with beaconing activity may be determined based on the metrics. The profile of each entity may be compared with the beaconing metrics to determine the entities that may be engaged in beaconing activity. The entities may be characterized based on beaconing activity such that the beaconing activity includes communication with endpoints associated with the third parties, employing communication protocols associated with the third-parties, or exchanging payloads consistent with the beaconing activity. Reports that include information associated with the entities and its beaconing activity may be generated.

Identifying network entities based on beaconing activity

Embodiments are directed to monitoring network traffic associated with networks to provide metrics. A monitoring engine may determine an anomaly based on the metrics exceeding threshold values. An inference engine may be instantiated to provide an anomaly profile based on portions of the network traffic that are associated with the anomaly. The inference engine may provide an investigation profile based on the anomaly profile such that the investigation profile includes information associated with investigation activities associated with an investigation of the anomaly. The inference engine may monitor the investigation of the anomaly based on other portions of the network traffic such that the other portions of the network traffic are associated with monitoring an occurrence of the investigation activities. The inference engine may modify a performance score associated with the investigation profile based on the occurrence of the investigation activities and a completion status of the investigation.

Managing incident response operations based on monitored network activity

Embodiments are directed to monitoring network traffic using a network computer. The network computer provides anomaly information associated with anomalies that may be associated with monitored network traffic. An inference engine may determine the users associated with the anomalies based on the monitored network traffic. A communication channel associated with the users may be determined based on the anomalies and the monitored network traffic such that the communication channel may be separate from the monitored network traffic. The communication channel may be employed to provide investigative agents to the users. Investigative information may be collected from the investigative agents over the communication channel. The inference engine may provide a risk value that is associated with the anomalies based on the investigative information.

Automated risk assessment based on machine generated investigation

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Seattle, WA, United States of America

Edmund Hope Driggs