Acronis International Gmbh

Acronis Interna Banal Gmbh

Stanislav Protasov

Serguei M Beloussov

Vladimir Strogov

Mark Shmulevich

Alexander G Tormasov

Nikolay Grebennikov

Sergey Ulasen

Andrey Kulaga

Alexey Dod

Alexey Sergeev

Anatoly Stupak

Kirill Korotaev

Maxim V Lyadvinsky

Vyacheslav Levchenko

Alexey Kostyushko

Oleg Ishanov

Oleg Melnikov

Yuri Per

Anton Enakiev

Sanjeev Solanki

Oleg P Volkov

Vasily Semyonov

Andrey Zaitsev

Victor Lysenko

Eugene Aseev

Anastasia Pereberina

Igor Kornachev

Dmitry Chepel

Mark A Will

Vladimir Simonov

Mark Smulevich

Vitaly Pogosyan

Andrey Redko

Victor Shulga

Evgeny A Aseev

Oleg Shaikhatarov

Kailash Sivanesan

Alexander Andreev

Hiten Gajjar

Prabhuraj Reddy

Igor Barinov

Andrei Neporada

Vadim Karasev

Valery Chernyakovsky

Alexei Sergeev

Dian Bakti

Dmitry Martynov

Vladimir Miroshkin

Candid Wüest

Alexander Khalyapin

Alexey Borodin

Yee Chen Lim

Alexey Yakushkin

Andrey Panin

Dmitry Kogtev

Stanislav Roguine

Anton Tsvetkov

Valery Chernyakovskiy

Valeriy Chernyakovsky

Dmitry Gryaznov

Andrei Redko

Sergey Onuchin

Dulitha Gunasekera

Kirill Davydov

Serg Bell

Michael Chadwick

Alexey A Ruslyakov

Vladimir Zatsepin

Artem Shapiro

Vasyl Shandyba

Alexey Morlang

Alexander Snorkin

Victor Batraev

Andrey Adaschik

Alexey Malanov

Andrey Neporada

Mikhail Kochegarov

Alexey Danilov

Denis Dementev

Alexey Makhov

Georgy Gomenyuk

Andrey Polevoy

Tian Huat Tan

Mark Schmulevich

Lyudmila Ivanichkina

Vitaliy Pogosyan

Ramil Yusupov

Sergey Blintsov

Vitaly Halenchyk

Mark Shumlevich

Alexey Tyuryumov

Alexey Kuznetsov

Aliaksei Dodz

Valerii Cherniakovskii

Igor Tkachev

Konstantin Karikov

Sivanesan Kailash Prabhu

Sergey Lebedev

Aarthi Kannan

Xiaolu Hou

Disclosed are systems and methods for data archiving using machine learning techniques. The system collects statistical information and event data and processes them using machine learning techniques to classify data and/or predict data access demands. The system receives statistical information related to user access of a plurality of files, which can effectively 'train' the system to archive data that is not needed at a certain moment and extract it at other moments. The system identifies, using a machine learning module, a pattern of access in the plurality of files based on the received statistical information. The system modifies, using the identified pattern of access, a threshold value related to file access, and assigns a set of files from the plurality of files an access classification based on the modified threshold value. The system migrates the set of files between hot and cold data areas based on the assigned access classification.

System and method for data classification using machine learning during archiving

Disclosed herein are systems and methods for updating select files in an image backup. In an exemplary aspect, a method comprises performing an image backup of a storage device comprising a plurality of files. The method comprises selecting a file of the plurality of files based on file selection rules and subsequent to the image backup, detecting that the file has exited a full consistency state. The method comprises monitoring the file to detect a return to the full consistency state. In response to detecting that the file has returned to the full consistency state, the method comprises identifying a physical address of at least one sector comprising the file on the storage device, and updating a version of the file previously captured in the image backup with a version of the file after returning to the full consistency state.

Systems and methods for updating select files in an image backup

Disclosed herein are systems and method for detecting small objects in an image using a neural network (NN). An exemplary method may include: receiving a first NN that is trained on a dataset including a plurality of images depicting various objects; identifying a first structure of the first NN, the first structure indicative of each layer and layer size in the first NN; determining, based on the first structure, whether the first NN can classify an object less than a threshold size in an input image; in response to determining that the first NN cannot classify the object, identifying a subset of detection layers in the first NN; generating and training a second NN that has a second structure in which the subset of detection layers are replaced by at least one layer not in the subset; and receiving, from the second NN, a classification of the object.

Systems and methods for detecting small objects in an image using a neural network

Disclosed herein are systems and method efficiently executing a program operation on a cloud-based service. In an exemplary aspect, a method comprises receiving a request to perform a program operation on a cloud-based service and at least one user constraint for performing the program operation, and determining a plurality of sub-operations that are comprised in the program operation. The method comprises identifying a plurality of service component combinations offered by the service provider that can execute the program operation, and identifying, based on a status of each service component, at least one processing constraint of each service component. The method comprises determining, by a machine learning algorithm, a service component combination from the plurality of service component combinations for executing the program operation based on the at least one user constraint and processing constraints. The method comprises executing the program operation by the determined service component combination.

Systems and methods for optimized execution of program operations on cloud-based services

The present disclosure includes methods and systems for protecting network resources. A method may start, by a processor, copy-on-write snapshotting for modifications to a plurality of files stored on electronic storage. A method may monitor, by the processor, access to objects within a file system associated with the electronic storage for a set of operations. A method may intercept, by the processor, one or more operation of the set of operations for modifying a region of a file in the file system. A method may capture, by the processor, one or more of original contents, modified contents and written contents of the region. A method may end, by the processor, copy-on-write snapshotting. A method may perform malware and/or ransomware analysis on a process performing the modification to the region of the file in the file system.

System and method for protecting network resources

Disclosed herein are systems and method for performing failover during a cyberattack. In one exemplary aspect, a method comprises monitoring a computing device for the cyberattack and detecting that the cyberattack is in progress. While the cyberattack is in progress, the method comprises identifying a failover device that corresponds to the computing device, hardening the failover device to prevent the cyberattack from affecting the failover device, and performing failover by switching from the computing device to the failover device.

Systems and methods for performing failover during a cyberattack

Disclosed herein are systems and methods for indexing and searching an encrypted archive. In one exemplary aspect, a method comprises generating, by a hardware processor, an encrypted data archive based on a user backup performed using a backup plan with an encryption flag enabled and a user key; generating, by the hardware processor, an index key for the encrypted data archive; encrypting, by the hardware processor, the index key using the user key; storing, by the hardware processor, the index key in a secure data storage; creating and mounting, by the hardware processor, an encrypted file system folder for the encrypted data archive using the index key; decrypting, by the hardware processor, data in the encrypted data archive using the user key; and indexing, by the hardware processor, the decrypted data.

System and method for indexing and searching encrypted archives

Disclosed herein are systems and method for determining a backup schedule on a computer system. In one exemplary aspect, a method may comprise collecting user behavior data on the computer system. The method may comprise analyzing the user behavior data to determine an optimal time of a backup session to create backup copies of modified data stored on a volume of the computer system and determining an optimal duration of the backup session based on the analyzed user behavior. The method may comprise determining a portion of the modified data that can be saved during the backup session within the optimal duration at the optimal time of backup, and performing the backup session comprising the portion.

System and method of determining an optimized schedule for a backup session

Disclosed herein are systems and methods for preventing malicious injections. In one aspect, a method includes monitoring active processes that are running in suspended mode. For each active process being monitored, the method includes injecting a dynamic link library (DLL) into the active process to hook an application programming interface (API) of an application corresponding to the active process, wherein the DLL is injected for tracking commands for suspension and resumption of the active process. The method includes monitoring file inputs and outputs of the application for anomalies while the active process is in the suspended mode, and when a command for resuming the active process is detected using the DLL, determining, based on the monitoring, whether a malicious process is inserted into the active process. The method includes allowing the suspended process to resume execution in response to determining that no malicious process is inserted in the active process.

Systems and methods for preventing injections of malicious processes in software

Disclosed herein are systems and method for providing nested frontend applications in a user interface of a management application. An exemplary method may include: generating a graphical user interface (GUI) for the management application, wherein the GUI includes a first extension point that includes a plurality of extensions, wherein each extension of the plurality of extensions is a standalone frontend application; injecting, during runtime of the management application, a first extension into the first extension point based on a personal configuration file, wherein the first extension is included in the plurality of extensions after injection; in response to receiving, via the GUI, a selection of the first extension, generating, on the GUI, a second extension point corresponding to the first extension, wherein the second extension point includes an additional plurality of extensions; and injecting, during the runtime, a second extension into the second extension point.

Systems and methods for providing nested frontend applications for managed service providers

A system and method of anti-malware analysis including iterative techniques. These techniques are used to create a file attribute tree used by a machine learning analyzer to identify malicious files.

Machine learning through iterative memory analysis for malware detection

Disclosed herein are systems and method for anti-malware scanning, including identifying a plurality of objects in a backup archive that is connected to a first network comprising a plurality of computing devices; scanning the plurality of objects in the backup archive to generate a whitelist indicating a subset of the plurality of objects that do not need to be scanned at a subsequent time; performing, using the whitelist, a first malware scan in a computing device of the plurality of computing devices; detecting that the computing device has left the first network to join a second network; and performing a second malware scan on the computing device, wherein the second malware scan uses a different whitelist of the second network, and wherein the second malware scan comprises scanning a first object that is not in the different whitelist and was not scanned in the first malware scan.

Systems and methods for anti-malware scanning using automatically-created white lists

The IOC Infrastructure management system () and method is disclosed for building an IOC infrastructure and its management thereof. The system mainly includes a IOC processing unit and an endpoint engine. The IOC processing unit is configured to i) source raw IOCs from a plurality of external sources, ii) convert format of the raw IOCs into a predetermined format of an IOC database using a parser unit, where each parser of the parser unit corresponds to at least one IOC format, iii) build and apply syntax tree to the parsed IOCs, where the syntax tree supports complex expression-based toolsets, such as YARA, and sort the IOCs lexicographically to avoid duplication of IOC entry and render the malware detection scanning process faster and efficient.

IOC management infrastructure

A data storage system uses erasure coding in combination with hashgraph to organize stored data and recover that data in a computing environment.

Integration of hashgraph and erasure coding for data integrity

Disclosed herein are systems and method for selectively restoring a computer system to an operational state. In an exemplary aspect, the method may include creating a backup image of the computer system comprising a set of data blocks, detecting that the computer system has begun an initial startup, identifying a subset of the data blocks read from a disk of the computer system during the initial startup. In response to determining that the computer system should be restored, the method may include restoring the subset of the data blocks such that the computer system is operational during startup, and restoring a remaining set of the data blocks from the backup image after the startup of the computer system.

System and method of selectively restoring a computer system to an operational state

Growing community of inventors

Moscow, Russia

Stanislav Protasov