Abstract: Detecting malware attacks is described herein. A computer-implemented method may include receiving, via a processor, events from a plurality of activity monitors. The method also include extracting, via the processor, a plurality of behavioral features from the received events. The method may further include detecting, via the processor, a malware attack based on the extracted behavioral features using a malware identification model trained on private data and public data using a machine learning technique, wherein the private data includes private enterprise attack findings. The method may also include executing, via the processor, an ad hoc protection improvement based on the detected malware attack.

Abstract: Detecting malware attacks is described herein. A computer-implemented method may include receiving, via a processor, events from a plurality of activity monitors. The method also include extracting, via the processor, a plurality of behavioral features from the received events. The method may further include detecting, via the processor, a malware attack based on the extracted behavioral features using a malware identification model trained on private data and public data using a machine learning technique, wherein the private data includes private enterprise attack findings. The method may also include executing, via the processor, an ad hoc protection improvement based on the detected malware attack.

Abstract: Detecting malware attacks is described herein. A computer-implemented method may include receiving, via a processor, events from a plurality of activity monitors. The method also include extracting, via the processor, a plurality of behavioral features from the received events. The method may further include detecting, via the processor, a malware attack based on the extracted behavioral features using a malware identification model trained on private data and public data. The method may also include executing, via the processor, an ad hoc protection improvement based on the detected malware attack.

Abstract: Detecting malware attacks is described herein. A computer-implemented method may include receiving, via a processor, events from a plurality of activity monitors. The method also include extracting, via the processor, a plurality of behavioral features from the received events. The method may further include detecting, via the processor, a malware attack based on the extracted behavioral features using a malware identification model trained on private data and public data. The method may also include executing, via the processor, an ad hoc protection improvement based on the detected malware attack.

Abstract: A computer implemented system for automatically determining a reliability score of a data report. The system includes a data structure indicative of data lineage that includes data assets flowing into a data report. The system further includes a processor configured to calculate trust scores for data assets based on characteristics of the data assets as well as a data report trust density score indicative of the reliability score of the report based on the calculated trust scores. The system further includes an output device configured to output the calculated data report trust density score.

Abstract: A computer implemented system for automatically determining a reliability score of a data report. The system includes a data structure indicative of data lineage that includes data assets flowing into a data report. The system further includes a processor configured to calculate trust scores for data assets based on characteristics of the data assets as well as a data report trust density score indicative of the reliability score of the report based on the calculated trust scores. The system further includes an output device configured to output the calculated data report trust density score.

Abstract: A computer-executable application is provided with access to resources generated using a principal model. A decoration model associated with the principal model is instantiated for use by the application. The application is provided with access to an object of the decoration model responsive to a request by the application to access an object of the principal model. The decoration model object corresponds to the principal model object.

Abstract: A computer-executable application is provided with access to resources generated using a principal model. A decoration model associated with the principal model is instantiated for use by the application. The application is provided with access to an object of the decoration model responsive to a request by the application to access an object of the principal model. The decoration model object corresponds to the principal model object.

Abstract: A computer-executable application is provided with access to resources generated using a principal model. A decoration model associated with said principal model is instantiated for use by the application. The application is provided with access to an object of the decoration model responsive to a request by the application to access an object of the principal model. The decoration model object corresponds to the principal model object.

Abstract: A computer-executable application is provided with access to resources generated using a principal model. A decoration model associated with said principal model is instantiated for use by the application. The application is provided with access to an object of the decoration model responsive to a request by the application to access an object of the principal model. The decoration model object corresponds to the principal model object.

Abstract: A method for implementing a model-driven architecture, including defining a principal model having a plurality of classes, references, attributes, and associations between any of the classes, the model configured to facilitate the automatic generation of at least one resource for use by a computer-executable application, where a change to the principal model subsequent to performing the automatic generation requires the automatic generation be performed again in order to effect the change for use by the application, defining a decoration model having a class, reference, and attribute for any corresponding one of the primary model classes, references, and attributes, where a change to the decoration model subsequent to performing the automatic generation does not require the automatic generation be performed again in order to effect the change for use by the application, mapping the decoration model to the principal model, and storing both of the models on a computer-readable medium.

Abstract: A method for implementing a model-driven architecture, including defining a principal model having a plurality of classes, references, attributes, and associations between any of the classes, the model configured to facilitate the automatic generation of at least one resource for use by a computer-executable application, where a change to the principal model subsequent to performing the automatic generation requires the automatic generation be performed again in order to effect the change for use by the application, defining a decoration model having a class, reference, and attribute for any corresponding one of the primary model classes, references, and attributes, where a change to the decoration model subsequent to performing the automatic generation does not require the automatic generation be performed again in order to effect the change for use by the application, mapping the decoration model to the principal model, and storing both of the models on a computer-readable medium.