Abstract: A method for ransomware detection and prevention includes receiving an event stream associated with one or more computer system events, generating user-added-value knowledge data for one or more digital assets by modeling digital asset interactions based on the event stream, including accumulating user-added-values of each of the one or more digital assets, and detecting ransomware behavior based at least in part on the user-added-value knowledge, including analyzing destruction of the user-added values for the one or more digital assets.

Abstract: Methods and systems for vehicle fault detection include collecting operational data from sensors in a vehicle. The sensors are associated with vehicle sub-systems. The operational data is processed with a neural network to generate a fault score, which represents a similarity to fault state training scenarios, and an anomaly score, which represents a dissimilarity to normal state training scenarios. The fault score is determined to be above a fault score threshold and the anomaly score is determined to be above an anomaly score threshold to detect a fault. A corrective action is performed responsive the fault, based on a sub-system associated with the fault.

Abstract: Methods and systems for training a neural network model include processing a set of normal state training data and a set of fault state training data to generate respective normal state inputs and fault state inputs that each include data features and sensor correlation graph information. A neural network model is trained, using the normal state inputs and the fault state inputs, to generate a fault score that provides a similarity of an input to the fault state training data and an anomaly score that provides a dissimilarity of the input to the normal state training data.

Abstract: A computer-implemented method for implementing alert interpretation in enterprise security systems is presented. The computer-implemented method includes employing a plurality of sensors to monitor streaming data from a plurality of computing devices, generating alerts based on the monitored streaming data, employing an alert interpretation module to interpret the alerts in real-time, matching problematic entities to the streaming data, retrieving following events, and generating an aftermath graph on a visualization component.

Abstract: A computer-implemented method for implementing alert interpretation in enterprise security systems is presented. The computer-implemented method includes employing a plurality of sensors to monitor streaming data from a plurality of computing devices, generating alerts based on the monitored streaming data, and employing an alert interpretation module to interpret the alerts in real-time, the alert interpretation module including a process-star graph constructor for retrieving relationships from the streaming data to construct process-star graph models and an alert cause detector for analyzing the alerts based on the process-star graph models to determine an entity that causes an alert.

Abstract: A computer-implemented method for implementing alert interpretation in enterprise security systems is presented. The computer-implemented method includes employing a plurality of sensors to monitor streaming data from a plurality of computing devices, generating alerts based on the monitored streaming data, automatically analyzing the alerts, in real-time, by using a graph-based alert interpretation engine employing process-star graph models, retrieving a cause of the alerts, an aftermath of the alerts, and baselines for the alert interpretation, and integrating the cause of the alerts, the aftermath of the alerts, and the baselines to output an alert interpretation graph to a user interface of a user device.

Abstract: A method for ransomware detection and prevention includes receiving an event stream associated with one or more computer system events, generating user-added-value knowledge data for one or more digital assets by modeling digital asset interactions based on the event stream, including accumulating user-added-values of each of the one or more digital assets, and detecting ransomware behavior based at least in part on the user-added-value knowledge, including analyzing destruction of the user-added values for the one or more digital assets.

Abstract: A computer-implemented method for implementing alert interpretation in enterprise security systems is presented. The computer-implemented method includes employing a plurality of sensors to monitor streaming data from a plurality of computing devices, generating alerts based on the monitored streaming data, employing an alert interpretation module to interpret the alerts in real-time, matching problematic entities to the streaming data, retrieving following events, and generating an aftermath graph on a visualization component.

Abstract: A computer-implemented method for implementing alert interpretation in enterprise security systems is presented. The computer-implemented method includes employing a plurality of sensors to monitor streaming data from a plurality of computing devices, generating alerts based on the monitored streaming data, and employing an alert interpretation module to interpret the alerts in real-time, the alert interpretation module including a process-star graph constructor for retrieving relationships from the streaming data to construct process-star graph models and an alert cause detector for analyzing the alerts based on the process-star graph models to determine an entity that causes an alert.

Abstract: A computer-implemented method for implementing alert interpretation in enterprise security systems is presented. The computer-implemented method includes employing a plurality of sensors to monitor streaming data from a plurality of computing devices, generating alerts based on the monitored streaming data, automatically analyzing the alerts, in real-time, by using a graph-based alert interpretation engine employing process-star graph models, retrieving a cause of the alerts, an aftermath of the alerts, and baselines for the alert interpretation, and integrating the cause of the alerts, the aftermath of the alerts, and the baselines to output an alert interpretation graph to a user interface of a user device.