IP Library Granted Patent US 11,113,156
Granted Patent B2
US 11,113,156 · App. 16/245,114 · Granted Sep 7, 2021

Automated ransomware identification and recovery

Inventors: Karl Edward Brewer (Columbia, SC); Oscar Rudolph McNeese, Jr. (Pomaria, SC); Sameer Prakash Kamat (Columbia, SC)
Assignee: KASEYA US LLC
G06F11/1469G06F21/568G06F2201/84G06F2221/034
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Quick Facts
Patent No.
US 11,113,156
App. No.
16/245,114
Granted
Sep 7, 2021
Kind
B2
Abstract

A method for automated ransomware identification includes receiving a first series of data items for backup from a host system, identifying, using a heuristic, a first characteristic of the first series of data items, receiving a second series of data items for backup from the host system, identifying, using the heuristic, a second characteristic of the second series of data items, detecting that the second characteristic differs from the first characteristic in a manner consistent with a ransomware infection, and invoking a recovery procedure responsive to the detecting.

Claims (57)

1. A method of managing a client system, comprising:

recording back-up data for the client system;

analyzing the recorded back-up data;

wherein analyzing the recorded back-up data comprises:

identifying an inconsistent data storage pattern based on a percentage of the back-up data being deduplicated, wherein the percentage of the back-up data being deduplicated diverges from computing systems related to the client system;

detecting, based on the analysis, malicious activity;

identifying, from the recorded back-up data, an infection point; and

restoring the client system to a state prior to the infection point, wherein the client system is restored based on snapshots in both cloud-based object storage and cloud-based block storage.

2. The method of claim 1 , wherein the recording the back-up data for the client system is performed periodically.

3. The method of claim 1 , wherein the malicious activity is ransomware activity.

4. The method of claim 1 , wherein analyzing the recorded back-up data comprises:

calculating an entropy of at least one back-up in the recorded back-up data; and

comparing the entropy of the back-up for the client system to an entropy of previous back-ups for the client system.

5. The method of claim 1 , wherein analyzing the recorded back-up data comprises:

identifying a predictable usage pattern for the back-up data for the client system; and determining a change in the predictable usage pattern based on the back-up data for the client system; and

detecting the malicious activity is based on the determined change in the predictable usage pattern.

6. The method of claim 1 , wherein analyzing the recorded back-up data comprises:

calculating an entropy of at least one back-up in the recorded back-up data; and

comparing the entropy of the back-up for the client system to an entropy of back-ups for at least one similar client system.

7. A method of managing a client system, comprising:

analyzing a recorded back-up data for the client system;

wherein analyzing the recorded back-up data comprises:

identifying an inconsistent data storage pattern based on a percentage of the back-up data being deduplicated, wherein the percentage of the back-up data being deduplicated diverges from computing systems related to the client system;

identifying an infection point using the recorded back-up data analysis;

generating a notification requesting authorization to restore from back-up data;

receiving authorization to proceed with recovery; and

restoring the client system to a state prior to the infection point, wherein the client system is restored based on snapshots in both cloud-based object storage and cloud-based block storage.

8. The method of claim 7 , further comprising generating a notification alerting an administrator to a potential problem.

9. The method of claim 7 , further comprising moving portions of the snapshot of the cloud-based object storage to the cloud-based block storage based on at least one recovery time objective.

10. The method of claim 7 , wherein detecting ransomeware activity comprises:

validating whether a file on the client system adheres to a file format designated for the file based at least in part on a file-type fingerprint; and

determining the file is non-conforming in response to the file not adhering to the file format designated for the file on the client system.

11. The method of claim 7 , wherein detecting ransomeware activity comprises:

maintaining a history of file modifications for the client system; and

analyzing file modification events to compare event trends to the history of file modifications for the client system.

12. The method of claim 7 , wherein detecting ransomeware activity comprises:

maintaining historical deduplication statistics for the client system; and

comparing new data deduplication statistics for the client system to the maintained historical deduplication statistics.

13. The method of claim 7 , wherein detecting ransomeware activity comprises:

determining whether a probability of infection exceeds a threshold probability based on factors indicating a probability of infection.

14. The method of claim 7 , wherein detecting ransomeware activity comprises:

maintaining historical deduplication data;

measuring an amount of new back-up deduplication data from the client system;

comparing the measured amount to historical deduplication data; and

determining ransomware activity in response to the measured amount being a statistical outlier from the historical deduplication data.

15. The method of claim 7 , wherein detecting ransomeware activity comprises:

maintaining historical deduplication data;

measuring an amount of new back-up deduplication data from the client system;

comparing the measured amount to historical deduplication data; and

determining ransomware activity in response to the measured amount exceeding an average amount of the historical deduplication data by at least a predetermined threshold.

16. The method of claim 7 , wherein detecting ransomeware activity comprises:

identifying an increase or change in types of file modification events corresponding to the client system.

17. The method of claim 7 , wherein detecting ransomeware activity comprises:

identifying an increase in randomness of the recorded back-up data.

18. The method of claim 7 , wherein detecting ransomeware activity comprises:

identifying a measure of consistency for the recorded back-up data; and

determining ransomware activity in response to a divergence of the recorded back-up data by more than the measure of consistency.

Assignments (5)
SECOND LIEN PATENT SECURITY AGREEMENT Recorded Mar 24, 2025
From: KASEYA US LLC
To: MORGAN STANLEY SENIOR FUNDING, INC., AS SECOND LIEN COLLATERAL AGENT
Reel/Frame 070608/0192 →
FIRST LIEN PATENT SECURITY AGREEMENT Recorded Mar 21, 2025
From: KASEYA US LLC
To: MORGAN STANLEY SENIOR FUNDING, INC., AS FIRST LIEN COLLATERAL AGENT
Reel/Frame 070586/0834 →
CHANGE OF NAME Recorded Oct 27, 2023
From: DATTO, INC.
To: DATTO, LLC
Reel/Frame 065385/0256 →
MERGER Recorded Aug 4, 2021
From: UNITRENDS, INC.
To: KASEYA US LLC
Reel/Frame 057083/0187 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 10, 2021
From: BREWER, KARL EDWARD; MCNEESE, OSCAR RUDOLPH, JR.; KAMAT, SAMEER PRAKASH
To: UNITRENDS, INC.
Reel/Frame 056190/0285 →
Cited By (3)
US 12,306,941 US 12,360,853 US 12,572,655