DYNAMIC POLICY BASED ON USER EXPERIENCE

• US 20190319961A1
• Filed: 04/12/2019
• Published: 10/17/2019
• Est. Priority Date: 04/13/2018
• Status: Active Grant

• Current Assignee: Sophos Limited (Sophos Group Ltd.)
• Original Assignee: Sophos Limited (Sophos Group Ltd.)
• Inventors: Levy, Joseph H., Thomas, Andrew J., Schiappa, Daniel Salvatore, Ray, Kenneth D.
• Granted Patent: US 11,087,014 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/137 Hash-based content-based indexing of textual data G06F16/31
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 21/45 Structures or tools for the administration of authentication
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: G06F 21/6218 to a system of files or objects, e.g. local or distributed file system or database
• CPC Class Codes: G06F 21/64 Protecting data integrity, e.g. using checksums, certificates or signatures
• CPC Class Codes: G06F 2221/034 Test or assess a computer or a system
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/0675 using electro-optical, acousto-optical or opto-electronic means
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: H04L 2463/082 applying multi-factor authentication cryptographic mechanisms or cryptographic arrangements including means for verifying the identity or authority of a user of the system or for message authentication H04L9/32
• CPC Class Codes: H04L 41/20 Network management software packages
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 63/08 for authentication of entities cryptographic mechanisms or cryptographic arrangements for entity authentication H04L9/32
• CPC Class Codes: H04L 63/0807 using tickets, e.g. Kerberos cryptographic mechanisms or cryptographic arrangements for entity authentication using tickets or tokens H04L9/3213
• CPC Class Codes: H04L 63/083 using passwords cryptographic mechanisms or cryptographic arrangements for entity authentication using a predetermined code H04L9/3226
• CPC Class Codes: H04L 63/0838 using one-time-passwords
• CPC Class Codes: H04L 63/0861 using biometrical features, e.g. fingerprint, retina-scan cryptographic mechanisms or cryptographic arrangements for entity authentication using biological data H04L9/3231
• CPC Class Codes: H04L 63/101 : Access control lists [ACL]
• CPC Class Codes: H04L 63/102 : Entity profiles
• CPC Class Codes: H04L 63/1408 : by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1416 : Event detection, e.g. attack signature detection
• 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 63/205 : involving negotiation or determination of the one or more network security mechanisms to be used, e.g. by negotiation between the client and the server or between peers or by selection according to the capabilities of the entities involved negotiation of communication capabilities H04L69/24
• CPC Class Codes: H04L 9/0891 : Revocation or update of secret information, e.g. encryption key update or rekeying
• CPC Class Codes: H04L 9/16 : the keys or algorithms being changed during operation
• CPC Class Codes: H04L 9/3213 : using tickets or tokens, e.g. Kerberos network architectures or network communication protocols for entities authentication using tickets in a packet data network H04L63/0807
• CPC Class Codes: H04L 9/3226 : using a predetermined code, e.g. password, passphrase or PIN network architectures or network communication protocols for supporting authentication of entities using passwords in a packet data network H04L63/083
• CPC Class Codes: H04L 9/3228 : One-time or temporary data, i.e. information which is sent for every authentication or authorization, e.g. one-time-password, one-time-token or one-time-key
• CPC Class Codes: H04L 9/3231 : Biological data, e.g. fingerprint, voice or retina network architectures or network communication protocols for supporting authentication of entities using biometrical features in a packet data network H04L63/0861
• CPC Class Codes: H04L 9/3265 : using certificate chains, trees or paths; Hierarchical trust model
• CPC Class Codes: H04L 9/3271 : using challenge-response
• CPC Class Codes: H04L 9/50 : using hash chains, e.g. blockchains or hash trees

Abstract

Entity models are used to evaluate potential risk of entities, either individually or in groups, in order to evaluate suspiciousness within an enterprise network. These individual or aggregated risk assessments can be used to adjust the security policy for compute instances within the enterprise network. A security policy may specify security settings such as network speed, filtering levels, network isolation, levels of privilege, and the like.

Claims

1. A computer program product comprising computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs the steps of:

storing an entity model for an entity at a threat management facility for an enterprise network, the entity including at least one of an identity and access management system, a domain controller, a physical device, a user, an operating system, or an application associated with the enterprise network, and the entity model characterizing a baseline of expected events based on events from the entity over an historical window as a vector in an event vector space;

instrumenting a compute instance associated with the entity to report event vectors based on one or more events from one or more sensors associated with the compute instance;

receiving an event stream at the threat management facility, the event stream including a plurality of event vectors from the compute instance;

calculating a risk score for the compute instance based on a distance between the entity model and one or more event vectors in the event stream in the event vector space; and

adjusting a policy for the compute instance based on the risk score, the policy including one or more security settings for the compute instance.

2. The computer program product of claim 1 wherein the historical window is algorithmically determined.

3. The computer program product of claim 1 wherein the policy includes a security setting for at least one of network speed, network communication filtering levels, network data quotas, levels of privilege, and network isolation.

4. The computer program of claim 1 wherein the threat management facility stores a plurality of entity models for a plurality of different entity types within the enterprise network.

5. The computer program product of claim 1 wherein the event stream includes event vectors from a plurality of compute instances associated with the enterprise network.

6. The computer program product of claim 1 wherein the event stream includes event vectors from two or more different entities associated with the compute instance.

7. The computer program product of claim 1 further comprising code that performs the steps of monitoring the event stream and creating the entity model based on a baseline of event vectors for the entity in the event stream over an interval, wherein the interval is algorithmically determined.

8. The computer program product of claim 7 further comprising code that performs the step of refining the entity model based on additional event vectors in the event stream received after the entity model is created.

9. The computer program product of claim 7 wherein instrumenting the compute instance includes configuring the compute instance to normalize at least one of the events from at least one of the sensors.

10. The computer program product of claim 7 wherein instrumenting the compute instance includes configuring the compute instance to tokenize at least one of the events from at least one of the sensors.

11. The computer program product of claim 7 wherein instrumenting the compute instance includes configuring the compute instance to encrypt at least one of the events from at least one of the sensors.

12. The computer program product of claim 7 wherein instrumenting the compute instance includes prioritizing at least one of the events from at least one of the sensors.

13. The computer program product of claim 1 wherein the distance is at least one of a Mahalanobis distance, a Euclidean distance, and a Minkowski distance.

14. The computer program product of claim 1 wherein the distance is evaluated using a k-nearest neighbor algorithm.

15. A method comprising:

storing an entity model at a threat management facility for an enterprise network, the entity model characterizing expected events for an entity;

instrumenting a compute instance in the enterprise network to detect one or more events and report a number of event vectors including the one or more events to the threat management facility;

receiving an event stream of the number of event vectors from the compute instance at the threat management facility;

calculating a risk score for the compute instance based on a comparison of one or more of the event vectors in the event stream with the entity model for the entity; and

adjusting a policy for the compute instance based on the risk score.

16. The method of claim 15 wherein the policy includes one or more security settings.

17. The method of claim 16 wherein the security settings specify at least one of network speed, network communication filtering levels, network data quotas, levels of privilege, and network isolation.

18. The method of claim 15 wherein the threat management facility stores a plurality of entity models for a plurality of different entity types within the enterprise network.

19. The method of claim 15 wherein the event stream includes event vectors from a plurality of compute instances associated with the enterprise network.

20. The method of claim 15 wherein the event stream includes event vectors from two or more different entities associated with the compute instance.

21. A system comprising:

a compute instance in an enterprise network, the compute instance configured to detect one or more events associated with the compute instance and to report an event vector including the one or more events to a remote resource; and

a threat management facility, the threat management facility including a memory storing an entity model characterizing expected events for an entity, and the threat management facility configured to receive an event stream including the event vector, to calculate a risk score for the compute instance based on a comparison of the event vector with the entity model, and to adjust a policy for the compute instance based on the risk score.