Model training and deployment in complex event processing of computer network data

• US 10,148,677 B2
• Filed: 04/18/2017
• Issued: 12/04/2018
• Est. Priority Date: 08/31/2015
• Status: Active Grant

• Current Assignee: Splunk Inc. (Cisco Systems, Inc.)
• Original Assignee: Splunk Inc. (Cisco Systems, Inc.)
• Inventors: Muddu, Sudhakar, Tryfonas, Christos
• Primary Examiner(s): Lwin, Maung T
• Application Number:
  US15/490,849
• Publication Number:
  US 20170223036A1
• Time in Patent Office: 595 Days
• Field of Search: None
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/444 Spatial browsing, e.g. 2D maps, 3D or virtual spaces
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/0484 for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04847 Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06F 40/134 Hyperlinking
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 10/225 based on a marking or identifier characterising the area
• CPC Class Codes: H04L 2463/121 Timestamp
• CPC Class Codes: H04L 41/0893 Assignment of logical groups to network elements
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 43/00 : Arrangements for monitoring or testing data switching networks
• CPC Class Codes: H04L 43/045 : for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/062 : related to network traffic
• CPC Class Codes: H04L 43/08 : Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 63/06 : for supporting key management in a packet data network cryptographic mechanisms or cryptographic arrangements for key management H04L9/08
• 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

Abstract

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

Claims

1. A method comprising:

computing in real-time a score by processing a stream of events through a first version of a machine learning model, wherein the stream of events corresponds to a time slice and includes time stamped machine data produced by a component within an information environment and reflects activity within the information technology environment, and wherein the machine learning model is configured to be trained by computer network activity characterized by the stream of events involving at least one entity;

training, in parallel with said processing the stream of events, a second version of the machine learning model with the time slice that is being processed through the first version for scoring, wherein said training includes retraining a model state of the second version of the machine learning model when a group-specific stream of events provides additional event feature sets;

invoking a model readiness logic to determine whether the second version of the machine learning model has sufficient training; and

performing live-swapping in the second version of the machine learning model to replace the first version of the machine learning model as an active version to compute another score, said live-swapping being based on a determination of whether the second version of the machine learning model is ready for active deployment.

2. The method of claim 1, wherein the determination of whether the second version is ready is based on at least one of:

a number of events that have been used to train the second version, a length of time that the second version has been in training, or whether the model state of the second version is converging.

3. The method of claim 1, wherein said live-swapping comprises processing a subsequent time slice from the stream of events through the second version of the machine learning model.

4. The method of claim 1, further comprising processing the time slice of the stream of events through the first version of the machine learning model prior to when a subsequent time slice becomes available.

5. The method of claim 1, wherein the stream of events are created based on raw event data pertaining to a plurality machine-observed events.

6. The method of claim 1, wherein the time slice being processed by the first version of the machine learning model is a most recent time slice from the stream of events.

7. The method of claim 1, wherein the time slice includes event feature sets that correspond to an event in the stream of events.

8. The method of claim 1, further comprising training the machine learning model by processing a previous time slice of the stream of events, prior to said computing the score.

9. The method of claim 1, wherein said processing the time slice through the first version of the machine learning model includes processing the time slice through a model deliberation process logic configured by the first version of the machine learning model;

wherein said training the second version of the machine learning model includes processing the time slice through a model training process logic; and

wherein the model training process logic and the model deliberation process logic are both machine learning models of a particular type.

10. The method of claim 1, wherein

said live swapping includes re-configuring, without first terminating, a model deliberation process thread that is performing said computing, said computing being performed in real-time.

11. The method of claim 1, furthering comprising processing the stream of events through a plurality of machine learning models of different types to detect security-related anomalies or threats of different types.

12. The method of claim 1, further comprising:

identifying a security-related anomaly or a security-related threat to enable remediation of the anomaly or threat as the stream of events is processed, the stream of events being processed in real-time.

13. The method of claim 1, wherein the machine learning model is specific to a particular entity involved in the stream of events, wherein the entity is a user, a device, a system, a network resource locator, an application, a process thread, or any combination thereof.

14. The method of claim 1, wherein the machine learning model performs behavioral analysis of a particular entity involved in the stream of events.

15. The method of claim 1, wherein the machine learning model performs peer group analysis amongst entities involved in the stream of events.

16. The method of claim 1, wherein the machine learning model performs time series analysis on a stream of the events.

17. The method of claim 1, wherein the machine learning model performs graph correlation analysis amongst entities involved in the stream of events.

18. The method of claim 1, wherein the machine learning model includes a state machine specific to an entity involved in an event represented in the time slice.

19. The method of claim 1, wherein said computing the score includes processing the stream of events through the machine learning model according to model deliberation processing logic specified by a model type associated with the machine learning model.

20. The method of claim 1, further comprising training the machine learning model according to model training processing logic specified by a model type associated with the machine learning model.

21. The method of claim 1, further comprising training the machine learning model according to model training processing logic specified by a model type associated with the machine learning model, wherein a model type associated with the machine learning model includes definitions for a model training workflow and a model deliberation workflow.

22. The method of claim 1, further comprising incrementally updating the machine learning model in real-time using the time slice of the events, wherein said incremental updating includes:

isolating a portion of a model state representative of the machine learning model affected by the events; and

re-training only the portion of the model state.

23. The method of claim 1, further comprising simultaneously training multiple machine learning models, associated with different purposes or different entities, in real-time.

24. The method of claim 1, wherein computing the score is performed in a distributed computation system that implements a task-parallel distributed data processing engine.

25. The method of claim 1, further comprising training the machine learning model in a distributed computation system that implements a task-parallel distributed data processing engine.

26. The method of claim 1, further comprising training the machine learning model in real-time using single-pass training processing logic.

27. The method of claim 1, further comprising:

training the machine learning model; and

storing a model state of the machine learning model, resulting from said training, in a distributed cache for use in said computing of the score.

28. The method of claim 1, wherein a threshold defining sufficiency of training is defined by the machine learning model.

29. A system comprising:

a communication device for receiving a stream of events; and

at least one hardware processor configured to;

compute in real-time a score by processing the stream of events through a first version of a machine learning model, wherein the stream of events corresponds to a time slice and includes time stamped machine data produced by a component within an information environment and reflects activity within the information technology environment, and wherein the machine learning model is configured to be trained to by computer network activity characterized by the stream of events involving at least one entity;

train, in parallel with said processing the stream of events, a second version of the machine learning model with the time slice that is being processed through the first version for scoring, wherein said training includes retraining a model state of the second version of the machine learning model when a group-specific stream of events provides additional event feature sets;

invoke a model readiness logic to determine whether the second version of the machine learning model has sufficient training; and

perform live-swapping in the second version of the machine learning model to replace the first version of the machine learning model as an active version to compute another score, said live-swapping being based on a determination of whether the second version of the machine learning model is ready for active deployment.

30. A non-transitory computer readable medium storing instructions, execution of which by a processor in a computer system causes the computer system to:

compute in real-time a score by processing a stream of events through a first version of a machine learning model, wherein the stream of events corresponds to a time slice and includes time stamped machine data produced by a component within an information environment and reflects activity within the information technology environment, and wherein the machine learning model is configured to be trained by computer network activity characterized by the stream of events involving at least one entity;

train, in parallel with said processing the stream of events, a second version of the machine learning model with the time slice that is being processed through the first version for scoring, wherein said training includes retraining a model state of the second version of the machine learning model when a group-specific stream of events provides additional event feature sets;

invoke a model readiness logic to determine whether the second version of the machine learning model has sufficient training; and

perform live-swapping in the second version of the machine learning model to replace the first version of the machine learning model as an active version to compute another score, said live-swapping being based on a determination of whether the second version of the machine learning model is ready for active deployment.