Abstract: A method for detecting and/or identifying a cyber-attack on a network can include segmenting the network using a segmentation method with machine learning to generate one or more network segments; assigning a score to a data point within each network segment based on a presence or absence of an identified anomalous behavior of the data point; analyzing network data flow, via behavioral modeling, to provide a context for characterizing the anomalous behavior; combining, via a reinforcement learning agent, outputs of the segmentation method with behavioral modelling and assigned score to detect and/or identify a cyber-attack; providing one or more alerts to an analyst; receiving an analyst assessment of an effectiveness of the detection and/or identification; and providing the analyst assessment as feedback to the reinforcement learning agent.

Abstract: A method for securing a network by applying one or more artificial intelligence (AI) models in a computing environment with a computing speed selected as a function of a bandwidth of the network. The method includes receiving data at a node associated with the network, and identifying a suspected cyber adversarial event at the network. The method includes applying an AI model on the data in real-time to enrich the data with information that indicates behavior associated with an exploitation of the network, and analyzing the enriched data as part of a cyber workflow for an indication of a compromise associated with the exploitation of the network.

Abstract: A method for detecting and/or identifying a cyber-attack on a network can include segmenting the network using a segmentation method with machine learning to generate one or more network segments; assigning a score to a data point within each network segment based on a presence or absence of an identified anomalous behavior of the data point; analyzing network data flow, via behavioral modeling, to provide a context for characterizing the anomalous behavior; combining, via a reinforcement learning agent, outputs of the segmentation method with behavioral modelling and assigned score to detect and/or identify a cyber-attack; providing one or more alerts to an analyst; receiving an analyst assessment of an effectiveness of the detection and/or identification; and providing the analyst assessment as feedback to the reinforcement learning agent.

Abstract: A method for securing a network by applying one or more artificial intelligence (AI) models in a computing environment with a computing speed selected as a function of a bandwidth of the network. The method includes receiving data at a node associated with the network, and identifying a suspected cyber adversarial event at the network. The method includes applying an AI model on the data in real-time to enrich the data with information that indicates behavior associated with an exploitation of the network, and analyzing the enriched data as part of a cyber workflow for an indication of a compromise associated with the exploitation of the network.

Abstract: A predictive engine for analyzing existing vulnerability information to determine the likelihood of a vulnerability being exploited by malicious actors against a particular computer or network of computers. The predictive engine relies on multiple data sources providing historical vulnerability information, a plurality of predictive models, and periodic retraining of the prediction ensemble utilizing predictive models. Modeling schemes may also be used when retraining the predictive models forming the prediction ensemble.

Abstract: A predictive engine for analyzing existing vulnerability information to determine the likelihood of a vulnerability being exploited by malicious actors against a particular computer or network of computers. The predictive engine relies on multiple data sources providing historical vulnerability information, a plurality of predictive models, and periodic retraining of the prediction ensemble utilizing predictive models. Modeling schemes may also be used when retraining the predictive models forming the prediction ensemble.