Using encrypted backup to protect files from encryption attacks
US-9990511-B1 · Jun 5, 2018 · US
US11244047B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11244047-B2 |
| Application number | US-201916579761-A |
| Country | US |
| Kind code | B2 |
| Filing date | Sep 23, 2019 |
| Priority date | Dec 19, 2016 |
| Publication date | Feb 8, 2022 |
| Grant date | Feb 8, 2022 |
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Official abstract text for this publication.
There is disclosed in one example a computing apparatus, including: a processor and a memory; a network interface to communicatively couple to a backup client; a storage to receive backup data from the client, including a plurality of versions and an associated reputation for each version, the associated reputation to indicate a probability that the version is valid; and instructions encoded within the memory to instruct the processor to: receive from the backup client a request to store a new version of the backup data; determine that the client has exceeded a backup threshold; identify a backup version having a lowest reputation for validity; and expunge the backup version having the lowest reputation for validity.
Opening claim text (preview).
What is claimed is: 1. A computing apparatus, comprising: a processor and a memory; a network interface to communicatively couple to a backup client; a storage to receive backup data from the client, including a plurality of versions and an associated reputation for each version, the associated reputation to indicate a probability that the version is valid; and instructions encoded within the memory to instruct the processor to: receive from the backup client a request to store a new version of the backup data; determine that the client has exceeded a backup threshold; identify a backup version having a lowest reputation for validity; and expunge the backup version having the lowest reputation for validity. 2. The computing apparatus of claim 1 , wherein the instructions are further to store the new version of the backup data with an associated reputation. 3. The computing apparatus of claim 1 , wherein the instructions are further to compute a reputation for the new version of the backup data. 4. The computing apparatus of claim 3 , wherein computing the reputation for the new version of the backup data comprises computing a delta between the new version and a previous version. 5. The computing apparatus of claim 4 , wherein the previous version is an immediate previous version. 6. The computing apparatus of claim 4 , wherein a magnitude of the delta is inversely proportional to the reputation. 7. The computing apparatus of claim 3 , wherein computing the reputation includes the use of contextual data. 8. The computing apparatus of claim 7 , wherein the contextual data include a data source. 9. The computing apparatus of claim 7 , wherein the contextual data include a reputation of software that initiated change to the data. 10. The computing apparatus of claim 7 , wherein the contextual data include entropy compared to a reference entity for a document or object type in the backup. 11. The computing apparatus of claim 7 , wherein the contextual data include a writing pattern of the data. 12. The computing apparatus of claim 3 , wherein the instructions are further to provide a machine learning model to compute the reputation. 13. One or more tangible, non-transitory computer-readable storage media having stored thereon executable instructions to instruct a processor to: allocate backup data from a client device to a backup store; associate with a plurality of backup versions in the backup store individual reputation scores, the individual reputation scores representing a reputation for validity for the backup versions; associate with the client a quota for backups; receive a new incoming backup from the client device; receive from the client a new backup version; determine that new backup version exceeds the client's quota for backups; identify within the backup store a backup version having a lowest reputation score; drop the backup version having the lowest reputation score; and add the new incoming backup version to the backup store. 14. The one or more tangible, non-transitory computer-readable media of claim 13 , wherein the instructions are further to compute a reputation for the new version of the backup data, wherein the reputation comprises contextual data. 15. The one or more tangible, non-transitory computer-readable media of claim 14 , wherein computing the reputation for the new version of the backup data comprises computing a delta between the new version and a previous version. 16. The one or more tangible, non-transitory computer-readable media of claim 15 , wherein the previous version is an immediate previous version. 17. The one or more tangible, non-transitory computer-readable media of claim 14 , wherein a magnitude of the delta is inversely proportional to the reputation. 18. A computer-implemented method for remediating ransomware attacks on incremental backups, comprising: receiving a plurality of backup versions from a client device; associating with the backup versions individual reputations for validity; receiving a new incremental backup version that exceeds a backup quota for the client device; identifying from among the plurality of backup versions a backup version with a lowest reputation for validity; removing the backup version with the lowest reputation for validity; storing the new incremental backup version; computing for the new incremental backup version a new reputation for validity; and associating the new reputation for validity with the new incremental backup version. 19. The method of claim 18 , wherein computing the new reputation for validity comprises computing a delta with an immediate previous version, and assigning a reputation that varies inversely with the delta. 20. The method of claim 18 , wherein computing the new reputation for validity comprises accounting for contextual data about the new incremental backup.
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