Comm Vault Systems, Inc.

Sanath Kumar

Dmitriy Borisovich Zakharkin

Manoj Kumar Pradhan

Parag Gokhale

Rahul S Pawar

Manas Bhikchand Mutha

Sri Karthik Bhagi

Pavan Kumar Reddy Bedadala

Arun Prasad Amarendran

Prosenjit Sinha

Hetalkumar N Joshi

Hemant Mishra

Gopikannan Venugopalsamy

Arun Prabu Duraisamy

Vipul Pawale

Divakar Radhakrishnan

Pushpendra Singh

Rohit Sivadas

Jayasree Yakkala

Jaya Rama Raju Inavolu

Sunil Babu Telagamsetti

Adwait Vinay Thattey

Ransomware detection accuracy is improved by analyzing patterns of changes in filename extension counts, relative to each other, that occur in a file system over time. The disclosed approach is malware-agnostic and does not rely on recognizing malware extensions or on real-time monitoring of the target file system. Instead, during each successive backup job of the target file system, the disclosed technology counts different types of filename extensions and compares the counts to each other and to corresponding counts taken in earlier backup jobs. Preferably, the anomaly detection analysis uses machine learning to discern a behavior pattern of the file system, which indicates how filename extensions are distributed and how much they change between backup jobs over time.

Ransomware detection accuracy based on machine learning analysis of filename extension patterns

Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

Network storage backup using distributed media agents

An improved content indexing (CI) system is disclosed herein. For example, the improved CI system may include a distributed architecture of client computing devices, media agents, a single backup and CI database, and a pool of servers. After a file backup occurs, the backup and CI database may include file metadata indices and other information associated with backed up files. Servers in the pool of servers may, in parallel, query the backup and CI database for a list of files assigned to the respective server that have not been content indexed. The servers may then request a media agent to restore the assigned files from secondary storage and provide the restored files to the servers. The servers may then content index the received restored files. Once the content indexing is complete, the servers can send the content index information to the backup and CI database for storage.

Distributed framework for data proximity-based task splitting in a content indexing system

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Woodbridge, NJ, United States of America

Sanath Kumar