Abstract: Detecting emergent abnormal behavior in a computer network faster and more accurately allows for the security of the network against malicious parties to be improved. To detect abnormal behavior, outbound traffic is examined from across several devices and processes in the network to identify rarely communicated-with destinations that are associated with rarely-executed processes. As a given destination and process is used more frequently over time by the network, the level of suspicion associated with that destination and process is lowered as large groups of devices are expected to behave the same when operating properly and not under the control of a malicious party. Analysts are alerted in near real-time to the destinations associated with the activities deemed most suspicious.

Abstract: A set of candidate malicious activity identification models are trained and evaluated against a production malicious activity identification model to identify a best performing model. If the best performing model is one of the candidate models, then an alert threshold is dynamically set for the best performing model, for each of a plurality of different urgency levels. A reset threshold, for each urgency level, is also dynamically set for the best performing model.

Abstract: A set of candidate malicious activity identification models are trained and evaluated against a production malicious activity identification model to identify a best performing model. If the best performing model is one of the candidate models, then an alert threshold is dynamically set for the best performing model, for each of a plurality of different urgency levels. A reset threshold, for each urgency level, is also dynamically set for the best performing model.

Abstract: Detecting emergent abnormal behavior in a computer network faster and more accurately allows for the security of the network against malicious parties to be improved. To detect abnormal behavior, outbound traffic is examined from across several devices and processes in the network to identify rarely communicated-with destinations that are associated with rarely-executed processes. As a given destination and process is used more frequently over time by the network, the level of suspicion associated with that destination and process is lowered as large groups of devices are expected to behave the same when operating properly and not under the control of a malicious party. Analysts are alerted in near real-time to the destinations associated with the activities deemed most suspicious.