Ao Kaspersky Lab

Acronis International Gmbh

Kaspersky Lab, Zao

Vyacheslav Levchenko

Serguei M Beloussov

Vladimir Strogov

Stanislav Protasov

Sergey Ulasen

Anatoly Stupak

Nikolay Grebennikov

Alexey Dod

Maxim V Yudin

Igor Y Kumagin

Alexander V Kalinin

Valery Chernyakovsky

Andrey V Sobko

Alexander S Tarasenko

Alexey Malanov

Pavel L Polozov

Mark Shmulevich

Konstantin Y Manurin

Aliaksei Dodz

Valerii Cherniakovskii

A system and method of anti-malware analysis including iterative techniques that combine static and dynamic analysis of untrusted programs or files. These techniques are used to identify malicious files by iteratively collecting new data for static analysis through dynamic run-time analysis.

Iterative memory analysis for malware detection

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

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 herein are systems and method for malicious behavior detection in processing chains comprising identifying and monitoring events generated by a first process executing on a computing device; storing snapshots of data modified by any of the events; determining a level of suspicion for the first process, wherein the level of suspicion is a likelihood of the first process being attributed to malware based on the data modified by any of the events; in response to determining that the first process is not trusted based on the determined level of suspicion, identifying at least one sub-process of the first process; and restoring, from the snapshots, objects affected by the first process and the at least one sub-process.

Systems and methods for detecting malicious behavior in process chains

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

Methods for file archiving using machine learning are disclosed herein. An exemplary method comprises archiving a first file of a plurality of files from a storage server to a tiered storage system, training a machine learning module based on file access operations for the plurality of files, determining one or more rules for predicting access to the archived files using the machine learning module, determining a prediction of access of the archived file based on the one or more rules and retrieving the archived file from the tiered storage system into a file cache in the storage server based on the prediction of access.

System and method for file archiving using machine learning

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 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 method for malicious behavior detection in processing chains comprising identifying a chain of related processes executing on a computing device; for each respective process in the chain of related processes: monitoring events generated by the respective process; storing snapshots of data modified by any of the events; determining a level of suspicion for the respective process by applying an artificial intelligence (AI) model to the snapshots of data; determining whether the chain of related processes is trusted based on the determined levels of suspicion; and in response to determining that the chain of related processes is not trusted, restoring objects affected by the chain from the snapshots.

Systems and methods for analyzing process chains to detect malicious behavior

A method is provided for protecting a file server from a ransomware attack. An exemplary method comprises assigning a session identifier to a remote session initiated with the file server, monitoring operations associated with the session identifier, determining whether the operations are suspicious according to a policy, creating a volume-level snapshot of files on the file server, determining that encryption of the data is occurring when entropy of the monitored data is growing faster than the predetermined threshold rate, classifying the remote session as having a calculated degree of danger when the operations match operations contained in previously observed suspicious behavior patterns, interrupting the remote session when a combination of the degree of danger and the entropy is greater than a predetermined threshold value and restoring the data on the file server using the volume-level snapshot to a state prior to the encryption and dangerous activity.

System and method for protection against ransomware attacks

Disclosed are systems and methods for protecting a computer system from ransomware and malware by copying and backing up files using a volume filter. A storage stack of the computer system includes a file protector driver and a volume filter driver. The file protector driver monitors for potentially dangerous actions to the system's files. The volume filter driver tracks any requested changes to files on a block level, and makes backup copies of the modified blocks when the blocks change on a block level of the storage device.

Anti-malware protection using volume filters

Disclosed are system and method for executing calls to a file system of a computer. An exemplary method comprises intercepting, by a software agent, a call to the file system; determining one or more parameters of the call that identify its functionality; determining a priority of executing the call based on the one or more parameters; storing information about the call, the parameters and the priority into a database; selecting from the database, one or more calls for execution based at least in part on relative priorities of execution of the plurality of calls stored in the database; determining whether to execute or not to execute a selected call based on whether the selected call interferes with execution of an earlier selected call; and when the selected call does not interfere with execution of an earlier selected call, passing the selected call to the file system for execution.

System and method for executing calls from OS process to file system

Disclosed are systems and methods for blocking access to protected applications. An exemplary method includes: intercepting access by a process of first information to be displayed on the user's device; determining second information based on the interception of the access by the process, the second information associated with the process; determining a region on a display of the user's device associated with the first information; analyzing one or more intersections between the region and at least one graphic interface associated with the process; and blocking the access by the process to the first information based on the analysis of the one or more intersections between the region and the at least one graphic interface associated with the process.

System and method of blocking access to protected applications

Disclosed is a system and method for identifying a path in a tree data structure having a plurality of levels. An example method includes receiving a request from a software application to access a resource in a computer file system using a requested path; identifying a first element in the requested path; comparing the first element with nodes in a first level of the tree data structure to identify an exact match; if the first element does not have an exact match, comparing the first element with at least one mask node in the first level of the tree data structure to identify a match by mask; and if the first element does not match one of mask nodes in the first level of the tree data structure, determining that the requested path is not in the tree data structure.

System and method for identifying a file path using tree data structure

In a computer system, joint operation of multiple hypervisors is coordinated. A persistent hypervisor and a non-persistent hypervisor are executed. The non-persistent hypervisor is executed in the supervisor mode according to an operating regime controlled by a scheduler engine, and the persistent hypervisor is executed in the hypervisor mode under the control of a handler engine. The handler engine monitors, and responds, to an attempted mode transition of the processor between the hypervisor and supervisor modes, and coordinates the suspension and resumption, as appropriate, of the persistent hypervisor.

Growing community of inventors

Moscow, Russia

Vyacheslav Levchenko