Patents by Inventor Derek Soeder

Derek Soeder has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11797826
    Abstract: 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.
    Type: Grant
    Filed: December 18, 2020
    Date of Patent: October 24, 2023
    Assignee: Cylance Inc.
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andy Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Eric Petersen, Ming Jin, Ryan Permeh
  • Patent number: 11188646
    Abstract: 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.
    Type: Grant
    Filed: October 24, 2019
    Date of Patent: November 30, 2021
    Assignee: Cylance Inc.
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andy Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Yaroslav Oliinyk, Ryan Permeh
  • Publication number: 20210256350
    Abstract: 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.
    Type: Application
    Filed: December 18, 2020
    Publication date: August 19, 2021
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andy Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Eric Petersen, Ming Jin, Ryan Permeh
  • Patent number: 11074494
    Abstract: 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.
    Type: Grant
    Filed: November 7, 2016
    Date of Patent: July 27, 2021
    Assignee: Cylance Inc.
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andy Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Eric Petersen, Ming Jin, Ryan Permeh
  • Patent number: 10922604
    Abstract: 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.
    Type: Grant
    Filed: November 7, 2016
    Date of Patent: February 16, 2021
    Assignee: Cylance Inc.
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andy Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Eric Petersen, Ming Jin, Ryan Permeh
  • Patent number: 10685112
    Abstract: 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.
    Type: Grant
    Filed: May 5, 2017
    Date of Patent: June 16, 2020
    Assignee: Cylance Inc.
    Inventors: Xuan Zhao, Aditya Kapoor, Matthew Wolff, Andrew Davis, Derek Soeder, Ryan Permeh
  • Patent number: 10652252
    Abstract: Systems, methods, and articles of manufacture, including computer program products, are provided for classification systems and methods using modeling. In some example embodiments, there is provided a system that includes at least one processor and at least one memory including program code which when executed by the at least one memory provides operations. The operations can include generating a representation of a sequence of sections of a file and/or determining, from a model including conditional probabilities, a probability for each transition between at least two sequential sections in the representation. The operations can further include classifying the file based on the probabilities for each transition.
    Type: Grant
    Filed: September 26, 2017
    Date of Patent: May 12, 2020
    Assignee: Cylance Inc.
    Inventors: Jian Luan, Derek Soeder
  • Patent number: 10637874
    Abstract: 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.
    Type: Grant
    Filed: November 7, 2016
    Date of Patent: April 28, 2020
    Assignee: Cylance Inc.
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andrew Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Yaroslav Oliinyk, Ryan Permeh
  • Publication number: 20200057853
    Abstract: 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.
    Type: Application
    Filed: October 24, 2019
    Publication date: February 20, 2020
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andy Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Yaroslav Oliinyk, Ryan Permeh
  • Patent number: 10503901
    Abstract: 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.
    Type: Grant
    Filed: November 7, 2016
    Date of Patent: December 10, 2019
    Assignee: Cylance Inc.
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andy Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Yaroslav Oliinyk, Ryan Permeh
  • Patent number: 10339305
    Abstract: In one aspect there is provided a method. The method may include: determining that an executable implements a sub-execution environment, the sub-execution environment being configured to receive an input, and the input triggering at least one event at the sub-execution environment; intercepting the event at the sub-execution environment; and applying a security policy to the intercepted event, the applying of the policy comprises blocking the event, when the event is determined to be a prohibited event. Systems and articles of manufacture, including computer program products, are also provided.
    Type: Grant
    Filed: February 24, 2017
    Date of Patent: July 2, 2019
    Assignee: Cylance Inc.
    Inventors: Ryan Permeh, Derek Soeder, Matthew Wolff, Ming Jin, Xuan Zhao
  • Publication number: 20190188381
    Abstract: 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.
    Type: Application
    Filed: May 5, 2017
    Publication date: June 20, 2019
    Inventors: Xuan Zhao, Aditya Kapoor, Matthew Wolff, Andrew Davis, Derek Soeder, Ryan Permeh
  • Publication number: 20180322287
    Abstract: 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.
    Type: Application
    Filed: May 5, 2017
    Publication date: November 8, 2018
    Inventors: Xuan Zhao, Aditya Kapoor, Matthew Wolff, Andrew Davis, Derek Soeder, Ryan Permeh
  • Publication number: 20180097826
    Abstract: Systems, methods, and articles of manufacture, including computer program products, are provided for classification systems and methods using modeling. In some example embodiments, there is provided a system that includes at least one processor and at least one memory including program code which when executed by the at least one memory provides operations. The operations can include generating a representation of a sequence of sections of a file and/or determining, from a model including conditional probabilities, a probability for each transition between at least two sequential sections in the representation. The operations can further include classifying the file based on the probabilities for each transition.
    Type: Application
    Filed: September 26, 2017
    Publication date: April 5, 2018
    Inventors: Jian LUAN, Derek SOEDER
  • Publication number: 20180075349
    Abstract: 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.
    Type: Application
    Filed: November 7, 2016
    Publication date: March 15, 2018
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andrew Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Eric Petersen, Ming Jin, Ryan Permeh
  • Publication number: 20180075348
    Abstract: 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.
    Type: Application
    Filed: November 7, 2016
    Publication date: March 15, 2018
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andrew Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Eric Petersen, Ming Jin, Ryan Permeh
  • Publication number: 20180063169
    Abstract: 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.
    Type: Application
    Filed: November 7, 2016
    Publication date: March 1, 2018
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andrew Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Yaroslav Oliinyk, Ryan Permeh
  • Publication number: 20180060580
    Abstract: 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.
    Type: Application
    Filed: November 7, 2016
    Publication date: March 1, 2018
    Inventors: Xuan Zhao, Matthew Wolff, John Brock, Brian Wallace, Andrew Wortman, Jian Luan, Mahdi Azarafrooz, Andrew Davis, Michael Wojnowicz, Derek Soeder, David Beveridge, Yaroslav Oliinyk, Ryan Permeh
  • Publication number: 20170249459
    Abstract: In one aspect there is provided a method. The method may include: determining that an executable implements a sub-execution environment, the sub-execution environment being configured to receive an input, and the input triggering at least one event at the sub-execution environment; intercepting the event at the sub-execution environment; and applying a security policy to the intercepted event, the applying of the policy comprises blocking the event, when the event is determined to be a prohibited event. Systems and articles of manufacture, including computer program products, are also provided.
    Type: Application
    Filed: February 24, 2017
    Publication date: August 31, 2017
    Inventors: Ryan Permeh, Derek Soeder, Matthew Wolff, Ming Jin, Xuan Zhao