Gracefully handling endpoint feedback when starting to monitor

• US 10,642,997 B2
• Filed: 07/25/2018
• Issued: 05/05/2020
• Est. Priority Date: 07/26/2017
• Status: Active Grant

• Current Assignee: Forcepoint Federal Holdings LLC (Forcepoint LLC)
• Original Assignee: Forcepoint LLC
• Inventors: Ford, Richard A., Irvine, Ann, Reeve, Adam, Snyder, Russell, Shih, Benjamin
• Primary Examiner(s): Schwartz, Darren B
• Application Number:
  US16/045,297
• Publication Number:
  US 20190034625A1
• Time in Patent Office: 650 Days
• CPC Class Codes: G06F 11/3072 where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: G06F 21/552 involving long-term monitoring or reporting
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: G06F 21/602 Providing cryptographic facilities or services
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G06F 21/84 output devices, e.g. displays or monitors
• CPC Class Codes: G06F 2201/86 Event-based monitoring
• CPC Class Codes: G06F 2221/031 Protect user input by software means
• CPC Class Codes: G06F 2221/032 Protect output to user by software means
• CPC Class Codes: G06F 2221/034 Test or assess a computer or a system
• CPC Class Codes: H04L 2209/16 Obfuscation or hiding, e.g. involving white box
• CPC Class Codes: H04L 63/1408 by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/1433 Vulnerability analysis
• CPC Class Codes: H04L 63/1441 Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 67/025 for remote control or remote monitoring of applications
• CPC Class Codes: H04L 67/141 Setup of application sessions admission control or resource allocation in data switching networks H04L47/70
• CPC Class Codes: H04L 67/146 : Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding
• CPC Class Codes: H04L 67/289 : Intermediate processing functionally located close to the data consumer application, e.g. in same machine, in same home or in same sub-network
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A method, system and computer-usable medium for adaptively assessing risk associated with an endpoint, comprising: determining a risk level corresponding to an entity associated with an endpoint; selecting a frequency and a duration of an endpoint monitoring interval; collecting user behavior to collect user behavior associated with the entity for the duration of the endpoint monitoring interval via the endpoint; processing the user behavior to generate a current risk score for the entity; comparing the current risk score of the user to historical risk scores to determine whether a risk score of a user has changed; and changing the risk score of the user to the current risk score when the risk score of the user has changed.

Claims

1. A computer-implementable method for adaptively assessing risk associated with an endpoint, comprising:

determining a risk level corresponding to an entity associated with an endpoint;

selecting a frequency of when to perform an endpoint monitoring interval and a duration of the endpoint monitoring interval;

collecting user behavior associated with the entity for the duration of the endpoint monitoring interval via the endpoint;

processing the user behavior to generate a current risk score for the entity;

comparing the current risk score of the user to historical risk scores to determine whether a risk score of a user has changed;

changing the risk score of the user to the current risk score when the risk score of the user has changed; and

,

decreasing the frequency of the endpoint monitoring interval when the current risk scores of the user remain substantially the same over a plurality of endpoint monitoring intervals, the current risk scores remaining substantially the same being when the current risk scores are within +/−

10% of the historical risk scores.

2. The method of claim 1, wherein:

the endpoint comprises a risk-adaptive feature pack.

3. The method of claim 2, wherein:

the risk-adaptive feature pack comprises at least one of an event data detector module, an event data collector module and a risk-adaptive security policy.

4. The method of claim 1, further comprising:

decreasing the duration of the endpoint monitoring interval when the current risk scores of the user decline over a plurality of endpoint monitoring intervals.

5. The method of claim 1, further comprising:

increasing the frequency of the endpoint monitoring interval when the current risk scores of the user increase over a plurality of endpoint monitoring intervals.

6. A system comprising:

a processor;

a data bus coupled to the processor; and

a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for;

determining a risk level corresponding to an entity associated with an endpoint;

selecting a frequency of when to perform an endpoint monitoring interval and a duration of the endpoint monitoring interval;

collecting user behavior associated with the entity for the duration of the endpoint monitoring interval via the endpoint;

processing the user behavior to generate a current risk score for the entity;

comparing the current risk score of the user to historical risk scores to determine whether a risk score of a user has changed;

changing the risk score of the user to the current risk score when the risk score of the user has changed; and

,

decreasing the frequency of the endpoint monitoring interval when the current risk scores of the user remain substantially the same over a plurality of endpoint monitoring intervals, the current risk scores remaining substantially the same being when the current risk scores are within +/−

10% of the historical risk scores.

7. The system of claim 6, wherein:

the endpoint comprises a risk-adaptive feature pack.

8. The system of claim 7, wherein:

the risk-adaptive feature pack comprises at least one of an event data detector module, an event data collector module and a risk-adaptive security policy.

9. The system of claim 6, wherein the instructions executable by the processor are further configured for:

decreasing the duration of the endpoint monitoring interval when the current risk scores of the user decline over a plurality of endpoint monitoring intervals.

10. The system of claim 6, wherein the instructions executable by the processor are further configured for:

increasing the frequency of the endpoint monitoring interval when the current risk scores of the user increase over a plurality of endpoint monitoring intervals.

11. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

determining a risk level corresponding to an entity associated with an endpoint;

selecting a frequency of when to perform an endpoint monitoring interval and a duration of the endpoint monitoring interval;

collecting user behavior associated with the entity for the duration of the endpoint monitoring interval via the endpoint;

processing the user behavior to generate a current risk score for the entity;

comparing the current risk score of the user to historical risk scores to determine whether a risk score of a user has changed;

changing the risk score of the user to the current risk score when the risk score of the user has changed; and

,

decreasing the frequency of the endpoint monitoring interval when the current risk scores of the user remain substantially the same over a plurality of endpoint monitoring intervals, the current risk scores remaining substantially the same being when the current risk scores are within +/−

10% of the historical risk scores.

12. The non-transitory, computer-readable storage medium of claim 11, wherein:

the endpoint comprises a risk-adaptive feature pack.

13. The non-transitory, computer-readable storage medium of claim 12, wherein:

the risk-adaptive feature pack comprises at least one of an event data detector module, an event data collector module and a risk-adaptive security policy.

14. The non-transitory, computer-readable storage medium of claim 11, wherein the computer executable instructions are further configured for:

decreasing the duration of the endpoint monitoring interval when the current risk scores of the user decline over a plurality of endpoint monitoring intervals.

15. The non-transitory, computer-readable storage medium of claim 11, wherein the computer executable instructions are further configured for:

increasing the frequency of the endpoint monitoring interval when the current risk scores of the user increase over a plurality of endpoint monitoring intervals.

16. The non-transitory, computer-readable storage medium of claim 11, wherein:

the computer executable instructions are deployable to a client system from a server system at a remote location.

17. The non-transitory, computer-readable storage medium of claim 11, wherein:

the computer executable instructions are provided by a service provider to a user on an on-demand basis.