Patents by Inventor Alexander S. Chistyakov

Alexander S. Chistyakov 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).

  • Publication number: 20190114419
    Abstract: Disclosed are systems and methods for detection of malicious files using machine learning. An example method comprises: selecting one or more data blocks in an object being analyzed based on rules; performing a static analysis on the one or more data blocks to determine a set of features of the one or more data blocks; determining a degree of harmfulness of the object based on the set of features and a model for detection of malicious objects, wherein the model has been trained by a method for machine learning on at least one safe object and one malicious object; recognizing the object is safe when the degree of harmfulness does not exceed a predetermined threshold of harmfulness; and recognizing the object is malicious when the degree of harmfulness of the one or more data blocks exceeds the predetermined threshold of harmfulness.
    Type: Application
    Filed: June 14, 2018
    Publication date: April 18, 2019
    Inventors: Alexander S. CHISTYAKOV, Ekaterina M. LOBACHEVA, Alexey M. ROMANENKO
  • Publication number: 20190114423
    Abstract: Disclosed are systems and methods generating a convolution function for training a malware detection model. An example method comprises selecting, by a processor, one or more commands from a log according to a set of predetermined rules, forming, by the processor, one or more behavior patterns from the one or more selected commands, determining, by the processor, a feature vector according to the one or more behavior patterns, generating, by the processor, a convolution function according to the feature vector, wherein a size of a result of the convolution function of the feature vector is less than the size of the feature vector, and computing, by the processor, one or more parameters for training a malware detection model using the convolution function on the one or more behavior patterns.
    Type: Application
    Filed: June 15, 2018
    Publication date: April 18, 2019
    Inventors: Alexander S. CHISTYAKOV, Ekaterina M. LOBACHEVA, Alexey M. ROMANENKO
  • Publication number: 20190114539
    Abstract: Disclosed are systems and methods generating a convolution function for training a malware detection model. An example method comprises generating, by a processor, a plurality of behavior patterns based on one or more logs of commands executed on a computing device, calculating, by the processor, an effectiveness of each of a plurality of methods for machine learning based on the plurality of behavior patterns, determining, by the processor, a preferred method for machine learning from the plurality of methods for machine learning by selecting the preferred method as a method with the greatest effectiveness from the plurality of methods for machine learning, obtaining, by the processor, parameters of the malware detection model by applying convolution functions to the plurality of behavior patterns, training, by the processor, the malware detection model to detect malicious files using the preferred method for machine learning.
    Type: Application
    Filed: June 12, 2018
    Publication date: April 18, 2019
    Inventors: Alexander S. CHISTYAKOV, Ekaterina M. LOBACHEVA, Alexey M. ROMANENKO
  • Publication number: 20190050567
    Abstract: The present disclosure provides a system for managing computer resources for detection of malicious files based on machine learning model. In one aspect, the system may comprise: a hardware processor configured to: form at least one behavior pattern on the basis of commands and parameters, calculate the convolution of the formed behavior pattern, calculate the degree of harmfulness the convolution and a model for detection of malicious files, manage the computing resources used to ensure the security of that computing device, based on the degree of harmfulness, wherein the degree of harmfulness is within a predetermined range of values and if the obtained degree of harmfulness of applications exceeds the predetermined threshold value, send a request to allocate additional resources of the computing device, otherwise send a request to free up previously allocated resources of the computing device.
    Type: Application
    Filed: July 19, 2018
    Publication date: February 14, 2019
    Inventors: Alexander S. Chistyakov, Ekaterina M. Lobacheva, Alexey M. Romanenko
  • Publication number: 20190018960
    Abstract: Disclosed are systems and methods for machine learning of a model for detecting malicious files. The described system samples files from a database of files and trains a detection model for detecting malicious files on the basis of an analysis of the sampled files. The described system forms behavior logs based on executable commands intercepted during execution of the sampled files, and generates behavior patterns based on the behavior log. The described system determines a convolution function based on the behavior patterns, and trains a detection model for detecting malicious files by calculating parameters of the detection model using the convolution function on the behavior patterns. The trained detection model may be used to detect malicious files by utilizing the detection model on a system behavior log generated during execution of suspicious files.
    Type: Application
    Filed: February 28, 2018
    Publication date: January 17, 2019
    Inventors: Alexander S. Chistyakov, Ekaterina M. Lobacheva, Alexey M. Romanenko