Cylance Inc.

Xuan Zhao

Matthew Wolff

Derek A Soeder

Ryan Permeh

Andrew Davis

Michael Thomas Wojnowicz

John Brock

Brian Michael Wallace

Jian Luan

David Neill Beveridge

Mahdi Azarafrooz

Ming Jin

Andy Wortman

Yaroslav Oliinyk

Samuel John Oswald

Mark Culley

Eric G Petersen

Steve Polson

Aditya Kapoor

Glenn Chisholm

Pedro Silva Do Nascimento Neto

Jonathan Wesley Miller

Li Li

Sepehr Akhavan-Masouleh

Andrew Wortman

Steven Polson

Brian Wallace

A system is provided for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

Machine learning model for analysis of instruction sequences

In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, are also disclosed.

Machine learning model for malware dynamic analysis

A system is provided for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Container file analysis using machine learning model

An endpoint computer system can harvest data relating to a plurality of events occurring within an operating environment of the endpoint computer system and can add the harvested data to a local data store maintained on the endpoint computer system. In some examples, the local data store can be an audit log and/or can include one or more tamper resistant features. Systems, methods, and computer program products are described.

Retention and accessibility of data characterizing events on an endpoint computer

An endpoint computer system can harvest data relating to a plurality of events occurring within an operating environment of the endpoint computer system and can add the harvested data to a local data store maintained on the endpoint computer system. A query response can be generated, for example by identifying and retrieving responsive data from the local data store. The responsive data are related to an artifact on the endpoint computer system and/or to an event of the plurality of events. In some examples, the local data store can be an audit log and/or can include one or more tamper resistant features. Systems, methods, and computer program products are described.

In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: training, based on a training data, a machine learning model to enable the machine learning model to determine whether at least one container file includes at least one file rendering the at least one container file malicious; and providing the trained machine learning model to enable the determination of whether the at least one container file includes at least one file rendering the at least one container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Training a machine learning model for container file analysis

Determining, by a machine learning model in an isolated operating environment, whether a file is safe for processing by a primary operating environment. The file is provided, when the determining indicates the file is safe for processing, to the primary operating environment for processing by the primary operating environment. When the determining indicates the file is unsafe for processing, the file is prevented from being processed by the primary operating environment. The isolated operating environment can be maintained on an isolated computing system remote from a primary computing system maintaining the primary operating system. The isolating computing system and the primary operating system can communicate over a cloud network.

Isolating data for analysis to avoid malicious attacks

In one respect, there is provided a system for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

In one respect, there is provided a system for training a neural network adapted for classifying one or more instruction sequences. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: training, based at least on training data, a machine learning model to detect one or more predetermined interdependencies amongst a plurality of tokens in the training data; and providing the trained machine learning model to enable classification of one or more instruction sequences. Related methods and articles of manufacture, including computer program products, are also provided.

Training a machine learning model for analysis of instruction sequences

In one respect, there is provided a system for training a neural network adapted for classifying one or more scripts. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one memory. The operations may include: extracting, from an icon associated with a file, one or more features; assigning, based at least on the one or more features, the icon to one of a plurality of clusters; and generating, based at least on the cluster to which the icon is assigned, a classification for the file associated with the icon. Related methods and articles of manufacture, including computer program products, are also provided.

Icon based malware detection

Under one aspect, a method is provided for protecting a device from a malicious file. The method can be implemented by one or more data processors forming part of at least one computing device and can include extracting from the file, by at least one data processor, sequential data comprising discrete tokens. The method also can include generating, by at least one data processor, n-grams of the discrete tokens. The method also can include generating, by at least one data processor, a vector of weights based on respective frequencies of the n-grams. The method also can include determining, by at least one data processor and based on a statistical analysis of the vector of weights, that the file is likely to be malicious. The method also can include initiating, by at least one data processor and responsive to determining that the file is likely to be malicious, a corrective action.

Protecting devices from malicious files based on n-gram processing of sequential data

In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

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Irvine, CA, United States of America

Xuan Zhao