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: 20220171880
    Abstract: A method for detecting a false positive outcome in classification of files includes, analyzing a file to determine whether or not the file is to be recognized as being malicious, analyzing a file to determine whether a digital signature certificate is present for the file, in response to recognizing the file as being malicious; comparing the digital certificate of the file with one or more digital certificates stored in a database of trusted files, in response to determining that the digital signature certificate is present for the file; and detecting a false positive outcome if the digital certificate of the file is found in the database of trusted files, when the false positive outcome is detected, excluding the file from further determination of whether the file is malicious and calculating a flexible hash value of the file.
    Type: Application
    Filed: February 15, 2022
    Publication date: June 2, 2022
    Inventors: Sergey V. Prokudin, Alexander S. Chistyakov, Alexey M. Romanenko
  • Patent number: 11288401
    Abstract: Disclosed herein are systems and methods for reducing a number of false positives in classification of files. In one aspect, an exemplary method comprises, analyzing a file to determine whether or not the file is to be recognized as being malicious, when the file is recognized as being malicious, analyzing the file to detect a false positive outcome, when the false positive outcome is detected, excluding the file from being scanned and calculating a flexible hash of the file, and storing the calculated flexible hash in a database of exceptions.
    Type: Grant
    Filed: September 11, 2019
    Date of Patent: March 29, 2022
    Assignee: AO Kaspersky Lab
    Inventors: Sergey V. Prokudin, Alexander S. Chistyakov, Alexey M. Romanenko
  • Publication number: 20220043910
    Abstract: Disclosed herein are methods and systems for selecting a detection model for detection of a malicious file. An exemplary method includes: monitoring a file during execution of the file within a computer system by intercepting commands of the file being executed and determining one or more parameters of the intercepted commands. A behavior log of the file being executed containing behavioral data is formed based on the intercepted commands and based on the one or more parameters of the intercepted commands. The behavior log is analyzed to form a feature vector. The feature vector characterizes the behavioral data. One or more detection models are selected from a database of detection models based on the feature vector. Each of the one or more detection models includes a decision-making rule for determining a degree of maliciousness of the file being executed.
    Type: Application
    Filed: October 12, 2021
    Publication date: February 10, 2022
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Patent number: 11227048
    Abstract: Disclosed herein are methods and systems for detecting malicious files. An exemplary method comprises emulating execution of a file under analysis, forming a behavior log of the emulated execution of the file under analysis, forming one or more behavior patterns from commands and parameters selected from the behavior log, calculating a convolution of the one or more behavior patterns, selecting two or more models for detecting malicious files from a database, calculating a degree of maliciousness of the file being executed based using the convolution and the two or more models, forming a decision making template based on the degree of maliciousness and determining that the file is malicious when a degree of similarity between the decision making template and a predetermined decision making template exceeds a predetermined threshold value.
    Type: Grant
    Filed: May 17, 2019
    Date of Patent: January 18, 2022
    Assignee: AO Kaspersky Lab
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Patent number: 11188649
    Abstract: Methods and systems are described in the present disclosure for classifying malicious objects. In an exemplary aspect, a method includes: collecting data describing a state of an object of the computer system, forming a vector of features, calculating a degree of similarity based on the vector, calculating a limit degree of difference that is a numerical value characterizing the probability that the object being classified will certainly belong to another class, forming a criterion for determination of class of the object based on the degree of similarity and the limit degree of difference, determining that the object belongs to the determined class when the data satisfies the criterion, wherein the data is collected over a period of time defined by a data collection rule and pronouncing the object as malicious when it is determined that the object belongs to the specified class.
    Type: Grant
    Filed: June 26, 2019
    Date of Patent: November 30, 2021
    Assignee: AO Kaspersky Lab
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Patent number: 11176250
    Abstract: Disclosed herein are methods and systems for detecting malicious files. An exemplary method comprises: forming a feature vector based on behavioral data of execution of a file, calculating parameters based on the feature vector using a trained model for calculation of parameters, wherein the parameters comprise: i) a degree of maliciousness that is a probability that the file may be malicious, and ii) a limit degree of safety that is a probability that the file will definitely prove to be malicious, wherein an aggregate of consecutively calculated degrees is described by a predetermined time law, deciding that the file is malicious when the degree of maliciousness and the limit degree of safety satisfy a predetermined criterion, wherein that criterion is a rule for the classification of the file according to an established correlation between the degree of maliciousness and the limit degree of safety.
    Type: Grant
    Filed: May 17, 2019
    Date of Patent: November 16, 2021
    Assignee: AO KASPERSKY LAB
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Patent number: 11036858
    Abstract: Methods and systems are described in the present disclosure for training a model for detecting malicious objects on a computer system. In an exemplary aspect, a method includes: selecting files from a database used for training a detection model, the selection is performed based on learning rules, performing an analysis on the files by classifying them in a hierarchy of maliciousness, forming behavior patterns based on execution of the files and parameters of the execution, training the detection model according to the analysis of the files and the behavior patterns, verifying the trained detection model using a test selection of files to test determinations of harmfulness of the test selection of files, and when the verification fails, retraining the detection model using a different set of files from the database, otherwise applying the detection model to a new set of files to determine maliciousness.
    Type: Grant
    Filed: July 2, 2019
    Date of Patent: June 15, 2021
    Assignee: AO Kaspersky Lab
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Publication number: 20210073418
    Abstract: Disclosed herein are systems and methods for reducing a number of false positives in classification of files. In one aspect, an exemplary method comprises, analyzing a file to determine whether or not the file is to be recognized as being malicious, when the file is recognized as being malicious, analyzing the file to detect a false positive outcome, when the false positive outcome is detected, excluding the file from being scanned and calculating a flexible hash of the file, and storing the calculated flexible hash in a database of exceptions.
    Type: Application
    Filed: September 11, 2019
    Publication date: March 11, 2021
    Inventors: Sergey V. Prokudin, Alexander S. Chistyakov, Alexey M. Romanenko
  • Patent number: 10929534
    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: Grant
    Filed: June 14, 2018
    Date of Patent: February 23, 2021
    Assignee: AO KASPERSKY LAB
    Inventors: Alexander S. Chistyakov, Ekaterina M. Lobacheva, Alexey M. Romanenko
  • Patent number: 10922410
    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: Grant
    Filed: June 15, 2018
    Date of Patent: February 16, 2021
    Assignee: AO KASPERSKY LAB
    Inventors: Alexander S. Chistyakov, Ekaterina M. Lobacheva, Alexey M. Romanenko
  • Patent number: 10878090
    Abstract: The present disclosure is directed to a system and method of detecting malicious files by using a trained machine learning model. The system may comprise a hardware processor configured to form at least one behavior pattern, calculate the convolution of all behavior patterns, select from a database of detection models at least two models for detection of malicious files on the basis of the behavior patterns, calculate the degree of harmfulness of a file being executed on the basis of an analysis of the convolution and the at least two models for detection of malicious files, form, on the basis of the degrees of harmfulness, a decision-making pattern, recognize the file being executed as malicious if the degree of similarity between the formulated decision-making pattern and at least one of a predetermined decision-making patterns from a database of decision-making patterns previously formulated on the basis of an analysis of malicious files, exceeds a predetermined threshold value.
    Type: Grant
    Filed: October 2, 2018
    Date of Patent: December 29, 2020
    Assignee: AO KASPERSKY LAB
    Inventors: Alexander S. Chistyakov, Ekaterina M. Lobacheva, Alexey M. Romanenko
  • Patent number: 10867042
    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: Grant
    Filed: June 12, 2018
    Date of Patent: December 15, 2020
    Assignee: AO KAPERSKY LAB
    Inventors: Alexander S. Chistyakov, Ekaterina M. Lobacheva, Alexey M. Romanenko
  • Patent number: 10831891
    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: Grant
    Filed: July 19, 2018
    Date of Patent: November 10, 2020
    Assignee: AO Kaspersky Lab
    Inventors: Alexander S. Chistyakov, Ekaterina M. Lobacheva, Alexey M. Romanenko
  • Patent number: 10795996
    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: Grant
    Filed: February 28, 2018
    Date of Patent: October 6, 2020
    Assignee: AO Kaspersky Lab
    Inventors: Alexander S. Chistyakov, Ekaterina M. Lobacheva, Alexey M. Romanenko
  • Publication number: 20200210570
    Abstract: Methods and systems are described in the present disclosure for classifying malicious objects. In an exemplary aspect, a method includes: collecting data describing a state of an object of the computer system, forming a vector of features, calculating a degree of similarity based on the vector, calculating a limit degree of difference that is a numerical value characterizing the probability that the object being classified will certainly belong to another class, forming a criterion for determination of class of the object based on the degree of similarity and the limit degree of difference, determining that the object belongs to the determined class when the data satisfies the criterion, wherein the data is collected over a period of time defined by a data collection rule and pronouncing the object as malicious when it is determined that the object belongs to the specified class.
    Type: Application
    Filed: June 26, 2019
    Publication date: July 2, 2020
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Publication number: 20200210567
    Abstract: Disclosed herein are methods and systems for detecting malicious files. An exemplary method comprises: selecting a file from a database of files used to perform training of a model for detecting a malicious file, forming one or more behavior patterns from intercepted one or more commands and parameters during execution of the file, forming a detection model, wherein the detection model selects a method of machine learning and is initialized with one or more hyper-parameters, training the detection model by calculating the one or more hyper-parameters based on the one or more behavior patterns to form a group of rules for calculating a degree of maliciousness of a resource and calculating a degree of maliciousness of another file based on the trained detection model.
    Type: Application
    Filed: May 17, 2019
    Publication date: July 2, 2020
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Publication number: 20200210577
    Abstract: Disclosed herein are methods and systems for detecting malicious files. An exemplary method comprises: forming a feature vector based on behavioral data of execution of a file, calculating parameters based on the feature vector using a trained model for calculation of parameters, wherein the parameters comprise: i) a degree of maliciousness that is a probability that the file may be malicious, and ii) a limit degree of safety that is a probability that the file will definitely prove to be malicious, wherein an aggregate of consecutively calculated degrees is described by a predetermined time law, deciding that the file is malicious when the degree of maliciousness and the limit degree of safety satisfy a predetermined criterion, wherein that criterion is a rule for the classification of the file according to an established correlation between the degree of maliciousness and the limit degree of safety.
    Type: Application
    Filed: May 17, 2019
    Publication date: July 2, 2020
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Publication number: 20200210576
    Abstract: Disclosed herein are methods and systems for detecting malicious files. An exemplary method comprises emulating execution of a file under analysis, forming a behavior log of the emulated execution of the file under analysis, forming one or more behavior patterns from commands and parameters selected from the behavior log, calculating a convolution of the one or more behavior patterns, selecting two or more models for detecting malicious files from a database, calculating a degree of maliciousness of the file being executed based using the convolution and the two or more models, forming a decision making template based on the degree of maliciousness and determining that the file is malicious when a degree of similarity between the decision making template and a predetermined decision making template exceeds a predetermined threshold value.
    Type: Application
    Filed: May 17, 2019
    Publication date: July 2, 2020
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Publication number: 20200210573
    Abstract: Methods and systems are described in the present disclosure for training a model for detecting malicious objects on a computer system. In an exemplary aspect, a method includes: selecting files from a database used for training a detection model, the selection is performed based on learning rules, performing an analysis on the files by classifying them in a hierarchy of maliciousness, forming behavior patterns based on execution of the files and parameters of the execution, training the detection model according to the analysis of the files and the behavior patterns, verifying the trained detection model using a test selection of files to test determinations of harmfulness of the test selection of files, and when the verification fails, retraining the detection model using a different set of files from the database, otherwise applying the detection model to a new set of files to determine maliciousness.
    Type: Application
    Filed: July 2, 2019
    Publication date: July 2, 2020
    Inventors: Alexander S. Chistyakov, Alexey M. Romanenko, Alexander S. Shevelev
  • Publication number: 20190114420
    Abstract: The present disclosure is directed to a system and method of detecting malicious files by using a trained machine learning model. The system may comprise a hardware processor configured to form at least one behavior pattern, calculate the convolution of all behavior patterns, select from a database of detection models at least two models for detection of malicious files on the basis of the behavior patterns, calculate the degree of harmfulness of a file being executed on the basis of an analysis of the convolution and the at least two models for detection of malicious files, form, on the basis of the degrees of harmfulness, a decision-making pattern, recognize the file being executed as malicious if the degree of similarity between the formulated decision-making pattern and at least one of a predetermined decision-making patterns from a database of decision-making patterns previously formulated on the basis of an analysis of malicious files, exceeds a predetermined threshold value.
    Type: Application
    Filed: October 2, 2018
    Publication date: April 18, 2019
    Inventors: Alexander S. CHISTYAKOV, Ekaterina M. LOBACHEVA, Alexey M. ROMANENKO