Abstract: A system and method for malware detection uses static and dynamic analysis to train a machine learning model. At the training step, static and dynamic features are extracted from training datasets and used to train a malware classification model. The malware classification model is used to classify unknown files based on verdicts from both static and dynamic models.

Abstract: Disclosed herein are systems and method for preventing the spread of malware in a synchronized data network, the method including: receiving, at a first time by a server connected to a plurality of computing devices, a file from a first computing device; monitoring for changes to the file stored on the server; in response to detecting a change, generating a record indicative of the change to the file; receiving, at the server from a second computing device, a download request for the file at a second time; determining whether at least one record exists that indicates any change to the file between the first time and the second time; in response to determining that the record exists, scanning the file for malware; and in response to determining that the file stored on the server is associated with malware, denying the download request.

Abstract: Disclosed herein are systems and method for restoring a clean backup after a malware attack. In one aspect, a method forms a list of files that are of a plurality of designated file types that can be infected by malicious software. The method performs one or more snapshots of the files according to a predetermined schedule over a predetermined period of time and performs one or more backups. The method determines that a malware attack is being carried out on the computing device and generates a list of dangerous objects that spread the malware attack. The method compares the list of dangerous objects with the one or more snapshots to determine when the malware attack occurred. The method identifies a clean backup that was created most recently before the malware attack as compared to other backups and recovers data for the computing device from the clean backup.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are systems and methods for preventing anti-forensics actions. In one exemplary aspect, a method may identify a suspicious object from a plurality of objects on a computing device and monitor actions performed by the suspicious object. The method may intercept a first command by the suspicious object to create and/or modify a digital artifact on the computing device and subsequent to intercepting the first command, intercept a second command by the suspicious object to delete at least one of the suspicious object and the digital artifact. In response to intercepting both the first command to create and/or modify the digital artifact and the second command to delete at least one of the suspicious object and the digital artifact, the method may block the second command, and may store the suspicious object and the digital artifact in a digital repository.

Abstract: Disclosed herein are systems and method for performing recovery using a backup image. In one exemplary aspect, a method comprises scanning a plurality of files on one or more storage devices of a computing device. The method may determine a first set of files from the plurality of files that will be used during recovery of the one or more storage devices, and tag a second set of files that will not be used during recovery. The method may copy the second set of files that have been tagged to an external storage device, and may store the first set of files in a backup image for the computing device (excluding the tagged second set of files from the backup image). The method may add, to the backup image, a respective link to each of the tagged second set of files in the external storage device.

Abstract: A system and method is provided for detecting a suspicious process in an operating system environment. In an exemplary aspect, a method comprises generating, by a hardware processor, a file honeypot in a directory in a file system and receiving a directory enumeration request from a process executing in the operating system environment. The method comprises determining whether the process is identified in a list of trusted processes and in response to determining that the process is not in the list of trusted processes, providing, to the process by the file system, a file list including the file honeypot responsive to the directory enumeration request. The method further comprises intercepting, by a file system filter driver, a file modification request for the file honeypot from the process, and identifying the process as a suspicious object responsive to intercepting the file modification request from the process.

Abstract: Disclosed herein are systems and method for scanning objects of a computing device, by an anti-malware, using a white list created for an organization based on data of the organization. In one aspect, an exemplary method comprises obtaining one or more objects of the organization from the computing device, and for each obtained object of the one or more objects, computing a hash value of the obtained object, determining whether the obtained object is whitelisted, and scanning the obtained object based on whether the obtained object is whitelisted, wherein the whitelist is created based on scanning of objects stored in archives of the organization, and the obtained object is determined as being whitelisted when the computed hash value of the obtained object matches a hash value of an object in a whitelist created for the organization.

Abstract: 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.

Abstract: Disclosed herein are systems and method for generating and storing forensics-specific metadata. In one aspect, a digital forensics module is configured to generate a backup of user data stored on a computing device in accordance with a backup schedule. The digital forensics module identifies, from a plurality of system metadata of the computing device, forensics-specific metadata of the computing device based on predetermined rules, wherein the forensics-specific metadata is utilized for detecting suspicious digital activity. The digital forensics module generates a backup of the forensics-specific metadata in accordance with the backup schedule and analyzes the forensics-specific metadata for an indication of the suspicious digital activity on the computing device. In response to detecting the suspicious digital activity based on the analysis, generates a security event indicating that the suspicious digital activity has occurred.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are systems and method for preventing malware reoccurrence when restoring a computing device using a backup image. In one exemplary aspect, a method may identify, from a plurality of backup images for a computing device, a backup image that was created most recently before the computing device was compromised. The method may mount the backup image as a disk and scanning the disk for malicious software. The method may disable all ports and services on the computing device to prevent unauthorized network connections and service launches. The method may restore data to the computing device from the mounted disk. The method may update software on the computing device and applying latest patches, and reopen the ports and restart the services on the computing device subsequent to updating the software and applying the latest patches.

Abstract: 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.

Abstract: Disclosed herein are systems and method for inspecting archived slices for malware using empty spare files. In one exemplary aspect, the method comprises generating a backup slice and a virtual volume comprising a list of files in the backup slice and associated file information. The method comprises mounting the virtual volume to a disk. The method comprises creating, in the virtual volume, empty sparse files that are placeholders of the files reference in the list of files. The method comprises detecting a change between a respective empty sparse file and a corresponding file in a previous backup slice and accordingly storing the actual content of the file in the virtual volume in place of the respective empty sparse file. The method comprises scanning the virtual volume for malicious software and generating a cured slice that replaces the backup slice in the backup archive upon detection.

Abstract: Aspects of the disclosure describe methods and systems for performing an antivirus scan using file level deduplication. In an exemplary aspect, prior to performing an antivirus scan on files stored on at least two storage devices, a deduplication module calculates a respective hash for each respective file stored on the storage devices. The deduplication module identifies a first file stored the storage devices and determines whether at least one other copy of the first file exists on the storage devices. In response to determining that another copy exists, the deduplication module stores the first file in a shared database, replaces all copies of the first file on the storage devices with a link to the first file in the shared database, and performs the antivirus scan on (1) the first file in the shared database and (2) the files stored on the storage devices.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure includes methods and systems for protecting network resources. An exemplary method comprises starting, by a processor, copy-on-write snapshotting for modifications to a plurality of files in storage, the modification initiated by a suspicious application, detecting, by the processor, a modification of a file of the plurality of files, determining, by the processor, whether the file is stored on a shared network resource or a local resource, in response to determining that the file is stored on a shared network resource, determining, by the processor, that a current region being modified is not already saved in a snapshot, and if the current region is not saved, saving the current region to a snapshot, marking, by the processor, the current region as being saved and analyzing all saved regions that were modified for malicious activity to determine that the suspicious application modifying the saved regions is malicious.