Acronis International Gmbh

Kaspersky Lab, Zao

Nikolay Grebennikov

Serguei M Beloussov

Stanislav Protasov

Vladimir Strogov

Andrey Kulaga

Vyacheslav Levchenko

Sergey Ulasen

Alexey Kostyushko

Anastasia Pereberina

Candid Wüest

Mark Shmulevich

Alexey Dod

Anatoly Stupak

Alexey V Monastyrsky

Oleg Ishanov

Eugene Aseev

Igor Kornachev

Valery Chernyakovsky

Mikhail A Pavlyushchik

Oleg V Zaitsev

Alexander A Gostev

Stanislav Roguine

Nikolay Denischenko

Aliaksei Dodz

Valerii Cherniakovskii

Danil Cherepanov

Disclosed herein are systems and method for detecting usage anomalies based on environmental sensor data. A method may include: receiving a physical user input at a computing device located in an environment; determining whether the physical user input was received from an authorized user of the computing device by: retrieving environmental sensor data from at least one sensor located in the environment; identifying a window of time during which the physical user input was received; and verifying a presence of the authorized user at the environment during the window of time based on the environmental sensor data; and in response to determining that the authorized user was not present in the environment during the window of time, detecting a usage anomaly and not executing the physical user input.

Systems and methods for detecting usage anomalies based on environmental sensor data

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 method for adjusting data protection levels based on system metadata. A method may include monitoring a computing device for a cyberattack, wherein a kernel driver of the computing device is configured to allow access to kernel control paths and hash tables in accordance with a first protection level, and detecting that the cyberattack is in progress. While the cyberattack is in progress, the method may include identifying kernel control paths and hashes of software objects that will be affected by the cyberattack, and configuring the kernel driver to disable access to the identified kernel control paths and hashes of the software objects in accordance with a second protection level, wherein the second protection level includes greater access restrictions to the computing device than the first protection level.

Systems and methods for adjusting data protection levels based on system metadata

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

Disclosed herein are systems and methods for classifying organizational structure for implementing data protection policies. In one exemplary aspect, a method may comprise retrieving a plurality of data files of an organization, wherein the plurality of data files are stored in a data storage; retrieving structural information of the organization, the structural information comprising details of user accounts, organizational roles, and file metadata within the organization; classifying the structural information into an organization type of a plurality of organization types; classifying each respective data file of the plurality of data files into a respective topic of a plurality of topics, wherein the plurality of topics are associated with the organization type; generating a data protection policy for the organization based on each respective topic of the plurality of data files and the organization type; and executing the data protection policy on the data storage.

Systems and methods for executing data protection policies specific to a classified organizational structure

Aspects of the disclosure describe methods and systems for cross-referencing forensic snapshots over time. In one exemplary aspect, a method may comprise receiving a first snapshot of a computing device at a first time and a second snapshot of the computing device at a second time and applying a pre-defined filter to the first snapshot and the second snapshot, wherein the pre-defined filter includes a list of files that are to be extracted from each snapshot. The method may comprise subsequent to applying the pre-defined filter, identifying differences in the list of files extracted from the first snapshot and the second snapshot. The method may comprise creating a change map for the computing device that comprises the differences in the list of files over a period of time, wherein the period of time comprises the first time and the second time, and outputting the change map in a user interface.

Systems and methods for cross-referencing forensic snapshot over time for root-cause analysis

Described herein are systems and methods for controlling access to a protected resource based on various criteria. In one exemplary aspect, a method comprises designating a plurality of program data installed on a computing system as protected program data; intercepting, by a kernel mode driver, a request from an untrusted application executing on the computing system to alter at least one of the protected program data; classifying, by a self-defense service, the untrusted application as a malicious application based on the intercepted request and information related to the untrusted application; and responsive to classifying the untrusted application as a malicious application, denying, by the kernel mode driver, access to the at least one of the protected program data.

Self-protection of anti-malware tool and critical system resources protection

Disclosed are systems and methods for detecting malicious applications. An exemplary method may comprise detecting that a first process has been launched on a computing device. The method may comprise receiving, from the first process, an execution stack associated with one or more control points of the first process. The method may comprise applying a machine learning classifier on the execution stack, wherein the machine learning classifier is configured to classify whether a process is malicious based on activity on control points captured on a given execution stack, and wherein a feature of a malicious process is detection of a system call to create a remote thread that runs in a virtual address space of a shared-service process configured to import third-party processes to be embedded as separate threads. The method may comprise generating an indication that the execution of the first process is malicious/non-malicious.

Systems and methods for detecting malicious processes

Disclosed are systems and methods for detecting multiple malicious processes. The described techniques identify a first process and a second process launched on a computing device. The techniques receive from the first process a first execution stack indicating at least one first control point used to monitor at least one thread associated with the first process, and receive from the second process a second execution stack indicating at least one second control point used to monitor at least one thread associated with the second process. The techniques determine that both the first process and the second process are malicious using a machine learning classifier on the at least one first control point and the at least one second control point. In response, the techniques generate an indication that an execution of the first process and the second process is malicious.

Systems and methods for protecting against malware code injections in trusted processes by a multi-target injector

Disclosed herein are systems and method for anti-virus scanning of backup data at a centralized storage. In an exemplary aspect, a method may receive, at the centralized storage, a backup slice from each respective computing device in a plurality of computing devices, wherein the centralized storage comprises, for each respective computing device, a respective backup archive including a plurality of backup slices. The method may mount the received backup slice as a virtual disk. The method may detect, for the respective computing device, a change between the mounted virtual disk and any number of previous backup slices and may evaluate the change against behavioral rules to identify malicious behavior. In response to determining that the change exhibits malicious behavior, the method may execute a remediation action to prevent an attack on the plurality of computing devices or the centralized storage.

Method and system for antivirus scanning of backup data at a centralized storage

Disclosed herein are systems and method for protecting an endpoint device from malware. In one aspect, an exemplary method comprises performing, by a light analysis tool of the endpoint, a light static analysis of a sample, terminating the process and notifying the user when the process is malware, performing light dynamic analysis when the process is not malware based on the light static analysis, when the process is clean based on the light dynamic analysis, enabling the process to execute, when the process is malware, terminating the process and notifying the user, and when the process is suspicious pattern, suspending the process, setting a level of trust, sending the sample to a sandbox, terminating the process and notifying the user when the process is a malware based on received final verdict, enabling the process to resume executing when the process is determined as being clean based on the final verdict.

Systems and methods for protecting devices from malware

Methods and systems for safeguarding against malware such as ransomware are described. In part, the disclosure relates to systems and methods for restoring user data and other data encrypted by malware or otherwise rendered inaccessible thereby. In one embodiment, the disclosure relates to a method of safeguarding user data. The method includes monitoring a plurality of processes executing on a computing device; detecting when a first process of the plurality of processes attempts to modify one or more parameters of a user data file; determining if first process is a trusted process or an untrusted process using one or more heuristics; and if the first process is determined to be an untrusted process, create a backup version of the user data file, wherein the backup version of the user data file is created with regard to an unchanged version the user data file.

Systems and methods of safeguarding user data

Disclosed are systems and methods for detecting malicious applications. The described techniques detect a first process has been launched on a computing device, and monitor at least one thread associated with the first process using one or more control points of the first process. An execution stack associated with the one or more control points of the first process is received from the first process. In response to detecting activity on the one or more control points of the first process, an indication that the execution of the first process is malicious is generated by applying a machine learning classifier to the received execution stack associated with the one or more control points of the first process.

Protecting against malware code injections in trusted processes

Disclosed herein are systems and methods for data remediation without data loss. In one exemplary aspect, the method comprises performing, at a first time, a first backup of a plurality of files on a file system of a computer system; tracking changes to any of the plurality of files on the file system after the first time; performing, at a second time, a second backup of the plurality of files on the file system; detecting, based on a scan of the second backup, an infection of the computer system caused by a malicious application; identifying, by the processor, a most recent backup of the file system that does not comprise the infection; in response to determining that the first backup is the most recent backup: restoring the first backup to the file system, and restoring a subset of files on the file system for which authorized changes.

System and method for data remediation without data loss

Disclosed herein are systems and method for correlating malware detections by endpoint devices and servers. In one aspect, an exemplary method comprises receiving, by a correlator, from one or more servers, one or more events collected without invasive techniques, one or more events collected using one or more invasive techniques, and one or more final verdicts, correlating the one or more events collected without invasive techniques with one or more events collected using the one or more invasive techniques, creating a suspicious pattern when an event of the one or more events collected without invasive techniques is correlated with an event of the one or more events collected using the one or more invasive techniques, and the event of the one or more events collected using one or more invasive techniques is used to detect a malware, and updating databases of one or more endpoint devices with created suspicious patterns.

Growing community of inventors

Moscow, Russia

Nikolay Grebennikov