LIGHT-WEIGHT INDEX DEDUPLICATION AND HIERARCHICAL SNAPSHOT REPLICATION

    公开(公告)号:US20210342297A1

    公开(公告)日:2021-11-04

    申请号:US16862470

    申请日:2020-04-29

    申请人: Rubrik, Inc.

    摘要: A lightweight deduplication system can perform resource efficient data deduplication using an extent index and a content index. The extent index can store full fingerprints of data segments to be deduplicated and the content index can store shortened versions of the full fingerprints. The system can alternate between the extent and content indexes, and cache portions of the indices to perform lightweight data deduplication. Further, the system can be configured with an efficient heuristic approach for selecting content index data lookups for chains of volumes for deduplication, such as a long chain of snapshots.

    Auto-upgrade of remote data management connectors

    公开(公告)号:US11663084B2

    公开(公告)日:2023-05-30

    申请号:US15672159

    申请日:2017-08-08

    申请人: RUBRIK, INC.

    摘要: Methods and systems for automatically upgrading or synchronizing a remote data management agent running on a remote host machine (e.g., a hardware server) to a particular version that is in-sync with a corresponding version used by a cluster of data storage nodes controlling the remote data management agent are described. The remote agent may be initially installed on the remote host and subsequent updates to the remote agent may be performed using the remote agent itself without requiring intervention by the remote host. The remote agent may comprise a backup agent and a bootstrap agent that are each exposed in different network ports or associated with different port numbers or networking addresses. The backup agent may perform data backup related tasks for backing up files stored on the remote host and the bootstrap agent may perform upgrade related tasks for upgrading the backup agent.

    Light-weight index deduplication and hierarchical snapshot replication

    公开(公告)号:US11321278B2

    公开(公告)日:2022-05-03

    申请号:US16862470

    申请日:2020-04-29

    申请人: Rubrik, Inc.

    摘要: A lightweight deduplication system can perform resource efficient data deduplication using an extent index and a content index. The extent index can store full fingerprints of data segments to be deduplicated and the content index can store shortened versions of the full fingerprints. The system can alternate between the extent and content indexes, and cache portions of the indices to perform lightweight data deduplication. Further, the system can be configured with an efficient heuristic approach for selecting content index data lookups for chains of volumes for deduplication, such as a long chain of snapshots.

    SCALABLE AUTOMATED TRAINING FRAMEWORK

    公开(公告)号:US20220247766A1

    公开(公告)日:2022-08-04

    申请号:US17162808

    申请日:2021-01-29

    申请人: Rubrik, Inc.

    摘要: Techniques for implementing a scalable automated training framework for anomaly and ransomware detection are disclosed. In some embodiments, a computer system performs operations comprising: instantiating a plurality of virtual machines, each one of the virtual machines being loaded with a corresponding file system; simulating user actions and ransomware on the virtual machines, the simulating of user actions and ransomware on the virtual machines causing changes to the corresponding file systems of the virtual machines; for each one of the plurality of virtual machines, generating a corresponding metadata file based on one or more corresponding snapshots of the virtual machine, the one or more corresponding snapshots indicating the changes to the corresponding file system of the virtual machine; and training a ransomware detection model using a machine learning algorithm and training data, the training data being based on the corresponding metadata files of the virtual machines.

    UNMASKING RANSOMWARE ATTACKS
    7.
    发明申请

    公开(公告)号:US20220245245A1

    公开(公告)日:2022-08-04

    申请号:US17162721

    申请日:2021-01-29

    申请人: Rubrik, Inc.

    摘要: Techniques unmasking ransomware attacks are disclosed. In some embodiments, a computer system performs operations comprising: generating a first prediction that a file system comprising a plurality of files has been attacked by ransomware based on snapshot metadata of the file system using a snapshot-level machine learning prediction model, the snapshot metadata comprising a plurality of file change data indicating a plurality of file change events that have been performed on the file system; in response to the first prediction, generating a classification for each one of the files based on the file change data using a file-level machine learning prediction model, the classification indicating whether the files have been targeted by the ransomware for encryption; determining that one or more files have been targeted by the ransomware based on the classification; and displaying the classification for the one or more files on a computing device of a user.