Abstract: An anomaly detection system uses an AI engine to analyze configurations and backups to identify and assess anomalies. Backup data and configurations are used to characterize events as either secure or insecure.

Abstract: Disclosed herein are systems and method for inspecting archived slices for malware. In one exemplary aspect, the method comprises identifying a first slice in a plurality of slices in a backup archive, wherein the first slice is an image of user data at a first time. The method comprises scanning the first slice of the plurality of slices in the backup archive and detecting at least one infected file in the first slice. The method comprises identifying a block of the first slice that corresponds to the at least one infected file. The method comprises mounting, to a disk, a second slice of the plurality of slices. The method comprises tracking the block and determining that the at least one infected file exists on the second slice and removing the infected file from the second slice by generating a respective cured slice of the second slice.

Abstract: Disclosed herein are systems and method for backing up data in a load-balanced clustered environment. A clustered resource to be backed up is selected, wherein the clustered resource is stored on a common storage system and operated on by a cluster-aware application executing on at least a first node and a second node of a computing cluster. A load-balanced application may migrate the clustered resource from the first node with a high-load consumption to the second node with low-load consumption. A list of changes made by both nodes are received and merged. A backup agent then generates a consistent incremental backup using data retrieved from the common storage system according to the merged list of changes to the clustered resource.

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: 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: 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.

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 in the shared-service process as separate threads. The method may comprise generating an indication that the execution of the first process is malicious/non-malicious.

Abstract: An anomaly detection system uses an AI engine to analyze configurations and backups to identify and assess anomalies. Backup data and configurations are used to characterize events as either secure or insecure.

Abstract: 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.

Abstract: Disclosed herein are systems and method for inspecting archived slices for malware. In one exemplary aspect, the method comprises identifying a first slice in a plurality of slices in a backup archive, wherein the first slice is an image of user data at a first time. The method comprises scanning the first slice of the plurality of slices in the backup archive and detecting at least one infected file in the first slice. The method comprises identifying a block of the first slice that corresponds to the at least one infected file. The method comprises mounting, to a disk, a second slice of the plurality of slices. The method comprises tracking the block and determining that the at least one infected file exists on the second slice and removing the infected file from the second slice by generating a respective cured slice of the second slice.

Abstract: Disclosed herein are systems and method for inspecting archived slices for malware. In one exemplary aspect, the method comprises mounting, to a disk, a first slice of a plurality of slices in a backup archive, wherein the first slice is an image of user data at a first time. The method further comprises detecting a modified block of the mounted, identifying at least one file in the mounted first slice that corresponds to the detected modified block, and scanning the at least one file for viruses and malicious software. In response to detecting that the at least one file is infected, the method comprises generating a cured slice that comprises the user data of the mounted first slice without the at least one file.

Abstract: Disclosed herein are systems and method for backing up data in a load-balanced clustered environment. A clustered resource to be backed up is selected, wherein the clustered resource is stored on a common storage system and operated on by a cluster-aware application executing on at least a first node and a second node of a computing cluster. A load-balanced application may migrate the clustered resource from the first node with a high-load consumption to the second node with low-load consumption. A list of changes made by both nodes are received and merged. A backup agent then generates a consistent incremental backup using data retrieved from the common storage system according to the merged list of changes to the clustered resource.

Abstract: Disclosed herein are systems and method for backing up data in a clustered environment. A clustered resource to be backed up is selected, wherein the clustered resource is stored on a common storage system and operated on by a cluster-aware application executing on two or more nodes of a computing cluster. A first backup agent executing on a first node of the computing cluster may determine a list of changes to the clustered resource and may receive at least one list of changes to the clustered resource that are tracked by peer backup agents executing on other nodes of the computing cluster. The first backup agent may merge the lists of changes to the clustered resource, and may generate a consistent incremental backup using data retrieved from the common storage system according to the merged lists of changes to the clustered resource.

Abstract: 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.

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: Methods and systems are disclosed herein for detecting malicious software executing on a plurality of computing devices. In an exemplary aspect, a method comprises collecting, from a plurality of agents executing on a respective computing device, analysis data corresponding to executables on the respective computing device, determining a suspicious activity pattern based on the received analysis data, determining that at least one executable on a computing device is malware based on the determined suspicious activity pattern, generating a plurality of remedial actions for protecting respective computing devices of the plurality of agents based on the suspicious activity pattern, and distributing, to the plurality of agents, the plurality of remedial actions to protect the respective computing device from the malware.

Abstract: Disclosed herein are systems and method for inspecting archived slices for malware. In one exemplary aspect, the method comprises mounting, to a disk, a first slice of a plurality of slices in a backup archive, wherein the first slice is an image of user data at a first time. The method further comprises detecting a modified block of the mounted, identifying at least one file in the mounted first slice that corresponds to the detected modified block, and scanning the at least one file for viruses and malicious software. In response to detecting that the at least one file is infected, the method comprises generating a cured slice that comprises the user data of the mounted first slice without the at least one file.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure provides systems and methods for optimizing lock detection in a change block tracker (CBT). A method comprises detecting changes on a disk volume and saving them to current changes, creating a volume changes and a snapshot changes collection, on a snapshot creation request, moving current changes to frozen changes, on a lock request, moving changes to the volume changes collection of the current snapshot, on another lock request, moving volume changes of current snapshot to frozen changes and deleting snapshot changes of current snapshot, when snapshot name is not the same as current snapshot, otherwise: moving the volume changes collection to the frozen changes, copying the frozen changes to the current changes, moving the snapshot changes collection to the frozen changes, and copying the snapshot changes collection to the current changes, moving all volume changes for other snapshots to current changes and allowing the client to read the frozen changes.