Abstract: Under one aspect, a computer-implemented method includes receiving a query at a query interface about whether a computer file comprises malicious code. It is determined, using at least one machine learning sub model corresponding to a type of the computer file, whether the computer file comprises malicious code. Data characterizing the determination are provided to the query interface. Generating the sub model includes receiving computer files at a collection interface. Multiple sub populations of the computer files are generated based on respective types of the computer files, and random training and testing sets are generated from each of the sub populations. At least one sub model for each random training set is generated.

Abstract: In one respect, there is provided a system for classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Abstract: In one respect, there is provided a system for classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Abstract: In one respect, there is provided a system for classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Abstract: In one respect, there is provided a system for classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Abstract: Under one aspect, a computer-implemented method includes receiving a query at a query interface about whether a computer file comprises malicious code. It is determined, using at least one machine learning sub model corresponding to a type of the computer file, whether the computer file comprises malicious code. Data characterizing the determination are provided to the query interface. Generating the sub model includes receiving computer files at a collection interface. Multiple sub populations of the computer files are generated based on respective types of the computer files, and random training and testing sets are generated from each of the sub populations. At least one sub model for each random training set is generated.

Abstract: A sample of data is placed within a directed graph that comprises a plurality of hierarchical nodes that form a queue of work items for a particular worker class that are used to process the sample of data. Subsequently, work items are scheduled within the queue for each of a plurality of workers by traversing the nodes of the directed graph. The work items are then served to the workers according to the queue. Results can later be received from the workers for the work items (the nodes of the directed graph are traversed based on the received results). In addition, in some variations, the results can be classified so that one or models can be generated. Related systems, methods, and computer program products are also described.