Sparkcognition, Inc.

Jarred Capellman

Lucas McLane

A method includes receiving, at a first server from a second server, a first file attribute associated with a file. The method includes making a determination, at the first server based on the first file attribute, of availability of a classification for the file from a cache of the first server. The method includes, in response to the determination indicating that the classification is not available from the cache, sending a notification to the second server indicating that the classification for the file is not available. The method also includes receiving a first classification for the file from the second server at the first server. The first classification is generated by the second server responsive to the notification.

Server-supported malware detection and protection

Translating text encodings of machine learning models to executable code, the method comprising: receiving a text encoding of a machine learning model; generating, based on the text encoding of the machine learning model, compilable code encoding the machine learning model; and generating, based on the compilable code, executable code encoding the machine learning model.

Translating text encodings of machine learning models to executable code

Automated malware detection for application file packages using machine learning (e.g., trained neural network-based classifiers) is described. A particular method includes generating, at a first device, a first feature vector based on occurrences of character n-grams corresponding to a first subset of files of multiple files of an application file package. The method includes generating, at the first device, a second feature vector based on occurrences of attributes in a second subset of files of the multiple files. The method includes sending the first feature vector and the second feature vector from the first device to a second device as inputs to a file classifier. The method includes receiving, at the first device from the second device, classification data associated with the application file package based on the first feature vector and the second feature vector. The classification data indicates whether the application file package includes malware.

Automated detection of malware using trained neural network-based file classifiers and machine learning

A processor-readable storage device storing instructions that cause a processor to perform operations including, subsequent to determining, at a first device based on a first file attribute associated with a file, that a classification for the file is unavailable at the first device, sending the first file attribute from the first device to a second device to determine whether the classification for the file is available at the second device. The operations include receiving a notification at the first device from the second device that the classification for the file is unavailable at the second device. The operations include, determining the classification for the file by performing, at the first device, an analysis of a second file attribute based on a trained file classification model. The operations include sending the classification from the first device to the second device and to a third device.

A method includes receiving a first file attribute from a computing device. The method also includes determining whether a classification for a file is available from a first cache of the server based on the first file attribute. The method includes sending the first file attribute from the server to a second server to determine whether the classification for the file is available at a base prediction cache of the second server. The method includes receiving a notification at the server from the second server that the classification for the file is unavailable at the base prediction cache. The method includes, in response to receiving the notification, determining the classification for the file by performing an analysis of a second file attribute based on a trained file classification model. The method includes sending the classification to the computing device and sending at least the classification to the base prediction cache.

A method includes receiving, at a server, a first file attribute from a computing device, the first file attribute associated with a file. The method also includes determining, based on the first file attribute, that a classification for the file is unavailable. The method further includes determining the classification for the file based on a trained file classification model accessible to the server and sending the classification to the computing device. The method includes sending at least the classification to a base prediction cache associated with a second server.

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Round Rock, TX, United States of America

Jarred Capellman