Ao Kaspersky Lab

Kaspersky Lab, Zao

Ao Kapersky Lab

Andrey A Efremov

Dmitry V Shmoylov

Anton S Lapushkin

Andrey V Ladikov

Pavel V Filonov

Alexey V Monastyrsky

Dmitry G Ivanov

Andrey Y Solodovnikov

Eldar M Kononov

Nikolay Denishchenko

Pavel A Zelensky

Yuri Mashevsky

Yuri V Namestnikov

Igor G Chekunov

Sergey V Kozlov

Dimitry G Ivanov

Disclosed herein are systems and methods for providing a trained model to a computing device of a user. In one aspect, an exemplary method comprises, receiving, by a model transmitter, registration information from the computing device of the user comprising a trained model of the user's behavior, wherein the model is constructed using software provided by a service, storing, by the model transmitter, the received registration information in a database of behavior models, and during a repeat visit, by the user, to the service, updating the trained model of the user's behavior and transmitting the updated trained model to the service, wherein the updated trained model differs from a previously sent model of the user's behavior by no more than is allowed for unambiguous identification of the user on the service.

System and method for providing a trained model to a computing device of a user

Disclosed herein are systems and methods for granting a user data processor access to a cryptocontainer of user data. In one aspect, an exemplary method comprises, creating a cryptocontainer for user's data, wherein the cryptocontainer receives at least one element of the user's data and encrypts the element; for the user data processor, establishing rights for accessing the element using a first key, and forming at least one access structure, the forming including, placing the first key in the access structure based on the established rights, receiving, from the user data processor, a second key linked to the user data processor which is to be used for accessing the first key, and encrypting the first key with the second key; and when a request for access to the cryptocontainer is received, granting, to the user data processor, access to the cryptocontainer based on the formed at least one access structure.

System and method of granting a user data processor access to a container of user data

Disclosed herein are systems and methods for selection of a model to describe a user. In one aspect, an exemplary method comprises, creating data on preferences of the user based on previously gathered data on usage of a computing device by the user and a base model that describes the user, wherein the base model is previously selected from a database of models including a plurality of models, determining an accuracy of the data created on the preferences of the user, wherein the determination is based on observed behaviors of the user, when the accuracy of the data is determined as being less than a predetermined threshold value, selecting a correcting model related to the base model, and retraining the base model, and when the accuracy of the data is determined as being greater than or equal to the predetermined threshold value, selecting the base model to describe the user.

System and method of selection of a model to describe a user

Disclosed herein are systems and methods for training a model to identify a user to a predetermined degree of reliability. In one aspect, an exemplary method comprises, parameterizing gathered data on behavior of a user in a form of a first vector, deriving a second vector from the first vector by removing noise and low-priority information from the first vector, providing the second vector to a training algorithm, and generating a trained model for the user, the generated trained model being different for each user such that only the trained model generated for the user satisfies the predetermined degree of reliability.

System and method of training a model to identify a user to a predetermined degree of reliability

Disclosed herein are systems and methods for generating individual content for a user of a service. In one aspect, an exemplary method comprises, gathering data on behavior of a user of a computing device, training a model of a user behavior based of the gathered data, wherein the trained data identifies the user to a predetermined degree of reliability, and generating an individual content for the user of the service based on a predetermined service environment in accordance with a trained model received from a model transmitter.

System and method of generating individual content for a user of a service

Disclosed herein are systems and method for sending user data in a client-server architecture with data anonymity and consistency. In an exemplary aspect, a client device may identify, a structure to send to the server, wherein the structure comprises the user data. The client device may divide the structure into two or more substructures and for each respective substructure of the two or more substructures, the client device may (1) assign a degree of confidentiality to the respective substructure and (2) send the respective substructure to a respective node of a plurality of nodes based on the assigned degree of confidentiality and a degree of security of the respective node. The respective node may be configured to apply a respective transformation to the respective substructure and transmit the transformed respective substructure to the server. The server may be configured to combine received transformed substructures into a transformed structure.

Systems and methods for anonymous and consistent data routing in a client-server architecture

Systems and methods are provided for detecting system anomalies. The described technique includes receiving system parameters specifying functionality of a computing system. An anomaly is detected within the computing system. A recovery method is determined based on a recovery-method model and information about the detected anomaly, responsive to detecting the anomaly in the computing system. The determined recovery method is configured to ensure requirements of the computing system are met. Furthermore, responsive to detecting the anomaly in the computing system, the determined recovery method is implemented in response to installation of the selected system-compatible tool.

Detection of system anomalies

Disclosed herein are systems and methods for granting access to data of a user. In one aspect, an exemplary method comprises, blocking the processing of data of a user, transferring the data of the user to a storage device, receiving a request for data processing from a collected data processor of a device, redirecting the received request to the storage device, determining, by the storage device, data access rights for the collected data processor of the device from which the request for data processing is received in accordance with data access rights established by a data access rights manager, and providing access to the data in accordance with the determined data access rights.

System and method of granting access to data of a user

A method for controlling secure access to user requested data includes retrieving information related to potential unauthorized access to user requested data. The information is collected by a plurality of sensors of user's mobile device. A trained statistical model representing an environment surrounding a user is generated based on the retrieved information. A first data security value is determined using the generated trained statistical model. The first data security value indicates a degree of information security based on user's environment. A second data security value is determined using the generated trained statistical model. The second data security value indicates a degree of confidentiality of the user requested data. The user requested data is filtered based on a ratio of the determined first data security value and the second data security value.

System and method for filtering user requested information

Systems and methods are presented for selection of compatible components for an observed system. An exemplary method comprises collecting parameters of one or more components of the system, assessing conformity of the one or more components of the system with a required state of the system, identifying one or more anomalies based on the assessment of conformity, analyzing the one or more anomalies to identify a class and parameters of the system corresponding to the one or more anomalies, determining one or more models of methods of restoration of the system, selecting one or more components that meets requirements of the one or more models of methods of restoration and implementing the one or more components in the system that are compatible with the system to eliminate the one or more anomalies.

System and method of asynchronous selection of compatible components

Systems and methods for anonymous collection of malware-related data from client devices. The system comprising a network node configured to (i) receive a first data structure from a client device, wherein the first data structure contain an identifier of the client device and an encrypted data that includes an identifier of a user of the client device and/or personal data of the user, and wherein the encrypted data was encrypted by the client device with a public key of the client device, wherein the public key was provided to the client device by an independent certification authority, (ii) transform the received first data structure by replacing the identifier of the client device with an anonymized identifier, and (iii) transmit the transformed first data structure containing the anonymized identifier and the encrypted data to a server.

System and method for anonymously collecting malware related data from client devices

Systems and methods for anonymously transmitting data in a network are provided, in which a request data structure is received by a network node from a client device. A first substructure containing personal data (PD) and a second substructure not containing PD are identified in the request data structure, by the network node. The first substructure is encrypted, by the network node, and is transmitted along with the second substructure to a server. A response data structure is received, by the network node, from the server. The first encrypted substructure and a third encrypted substructure are identified, by the network node, in the response data structure. The first encrypted substructure is decrypted, by the network node, and is transmitted along with the third encrypted substructure to the client device. The third encrypted substructure can be decrypted and viewed by the client device.

System and method for anonymously transmitting data in a network

Systems and methods for transmitting critical data to a server are provided. The data structure intended for transmission to the server is divided up on the client side into a substructure containing critical data (CD) and a substructure not containing CD. The substructure containing CD is further divided up at the client side into at least two substructures and the resulting substructures are sent consecutively to the server via a node with a transformation module. The substructure not containing CD is sent directly to the server, bypassing the node with the transformation module. After receiving the substructures, they are combined at the server side into a single data structure. The critical data are data with respect to which the law of the state in whose jurisdiction the client or an authorized entity is located imposes restrictions on the gathering, storage, accessing, dissemination and processing thereof.

System and method of transmitting confidential data

System and methods are provided for searching users that meet one or more search requirements. Configuration profiles are obtained of computing systems operated by sample users that have at least one determined characteristic. A machine learning model is generated that associates the determined characteristic of the sample users with the configuration profiles of the computing systems of the sample users. Identifying at least one target user that matches the at least one determined characteristic specified in a search query based on analysis of the configuration profile of the computing system of said target user by the machine learning model.

System and method of identifying and targeting users based on search requirements

Systems and methods for provided for detecting compatible modules for replacing anomalous elements in computing systems. The described technique includes receiving system parameters specifying functionality of a first computing system, and querying a state model using the received system parameters to detect an anomaly within the first computing system. In response to detecting an anomaly in the first computing system based on the state model, the system determines a recovery method based on a recovery-method model and information about the detected anomaly, and selecting, from a tool database, a third-party, system-compatible tool configured to implement the determined recovery method.

Growing community of inventors

Moscow, Russia

Andrey A Efremov