Event specific relationship graph generation and application in a machine data processing platform

• US 10,116,670 B2
• Filed: 01/27/2017
• Issued: 10/30/2018
• 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, Bulusu, Ravi Prasad
• Primary Examiner(s): Li, Meng
• Application Number:
  US15/418,546
• Publication Number:
  US 20170142140A1
• Time in Patent Office: 641 Days
• 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:

receiving, by a computer system, raw machine data produced by an information technology environment, the raw machine data indicative of activity of one or more components of the information technology environment;

wherein the received raw machine data include a plurality of data units, wherein the raw machine data in each data unit of the plurality of data units includes data indicative of an activity, entities that participated in the activity, and a timestamp for the activity;

for each data unit of the plurality of data units, by the computer system,

identifying a relationship between the entities indicated in the data unit, the relationship indicative of the activity indicated in the data unit, and

annotating, by using a data structure corresponding to a graph, the raw machine data in the data unit to incorporate data indicative of the relationship into the raw machine data in the data unit; and

providing, to an anomaly detection module, each of the plurality of data units including annotated raw machine data, for detection of a security-oriented anomaly in the information technology environment,

wherein the anomaly detection module is in a real-time path or a batch path, and wherein information regarding identified security-oriented anomalies is shared between the real-time path and the batch path.

2. The method of claim 1, wherein the relationship between the entities represents an activity, recorded in the raw machine data in the data unit, performed by one entity with respect to another entity.

3. The method of claim 1, further comprising:

sending the annotated raw machine data in the data unit to a distributed messaging system.

4. The method of claim 1, further comprising:

generating a composite relationship graph that is combined from the plurality of the identified relationships in the plurality of data units.

5. The method of claim 1, further comprising:

generating a composite relationship graph that is combined from the plurality of the identified relationships in the plurality of data units, wherein the detection of an anomaly in the information technology environment is performed based on a projection of the composite relationship graph.

6. The method of claim 1, wherein the relationship between the entities is deterministically identified according to actual records in the data unit.

7. The method of claim 1, further comprising:

utilizing, by the anomaly detection module, machine learning models to detect behavioral-based anomalies.

8. The method of claim 1, wherein the data indicative of the relationship is incorporated into the data unit by appending the graph to the raw machine data in the data unit.

9. The method of claim 1, further comprising:

extracting, at a subsequent stage, the data indicative of the relationship from the data unit in response to the data unit being examined.

10. The method of claim 1, wherein said identifying comprises:

tokenizing the raw machine data in the data unit by extracting, as tokens, a key, a value or a key-value pair from the raw machine data in the data unit; and

parsing the raw machine data in the data unit based on a predetermined data format that specifies which tokens represent the entities and which tokens represent activities in the extracted tokens.

11. The method of claim 1, wherein said identifying comprises:

parsing the raw machine data in the data unit based on a predetermined data format that specifies which data represent the entities and which data represent the activity in the data unit.

12. The method of claim 1, wherein said identifying comprises:

parsing the raw machine data in the data unit based on a predetermined data format that specifies which data represent the entities and which data represent the activity in the data unit,

wherein the predetermined data format comprises a data format representing output from at least one of;

an active directory, a proxy, a firewall, a web gateway, a virtual private network (VPN) connection, an intrusion detection system, a network traffic analyzer, or a malware engine.

13. The method of claim 1, wherein said identifying comprises:

tokenizing the raw machine data in the data unit by extracting, as tokens, a key or a value or a key-value pair in the data unit.

14. The method of claim 1, wherein the activity is performed by one entity with respect to another entity, and wherein the identified relationship between the entities is identified based on the action.

15. The method of claim 1, wherein the identified relationship between the entities is indicative of the action, and wherein the identified relationship is identified based on using the activity to reference into a data structure of identifiable relationships.

16. The method of claim 1, wherein said identifying step comprises:

detecting a data format of the raw machine data in the data unit.

17. The method of claim 1, further comprising:

issuing a query to a data processing system that performs the query against data stored in a distributed file system,

wherein said receiving step includes receiving the raw machine data in the data unit as a result of the query from the processing system.

18. The method of claim 1, further comprising:

issuing a query to a data processing system configured to execute the query against data stored in a distributed file system,

wherein said receiving includes receiving the raw machine data in the data unit as a result of the query from the processing system, and

wherein the data processing system includes a framework that provides methods including;

a map method to perform a data processing operation on local data on distributed nodes, and a reduce method to perform a summary operation to generate a result based on the processed local data.

19. The method of claim 1, further comprising:

requesting a machine on the computer network to transmit the raw machine data in the data unit, and wherein said receiving occurs in response to the machine on the computer network transmitting the raw machine data in the data unit.

20. The method of claim 1, wherein said identifying comprises identifying a timestamp in the data unit.

21. The method of claim 1, further comprising:

configuring said identifying by making an adjustment to a configuration file.

22. The method of claim 1, wherein the raw machine data in the data unit comprises timestamped machine data.

23. The method of claim 1, wherein the method is performed as part of an extract-transform-load stage of at least one of a distributed event processing system or an anomaly detection system.

24. The method of claim 1, further comprising:

identifying a plurality of attributes of the data unit, based on the raw machine data in the data unit; and

adding a view identifier to the raw machine data in the data unit to allow a downstream entity, by having designated the view identifier, to receive select information extracted from and/or generated based on the plurality of attributes of the data unit.

25. The method of claim 1, further comprising:

identifying a plurality of attributes of the data unit, based on the raw machine data in the data unit; and

adding a view identifier to the raw machine data in the data unit to allow a downstream entity, by having designated the view identifier, to receive select information extracted from and/or generated based on the plurality of attributes of the data unit,

wherein the interface further includes logic performs an activity on the attributes of the data unit to generate the information.

26. The method of claim 1, wherein each of the entities is of a type that is one of:

user, device, application, network resource locator, session, or threat; and

wherein at least two of the entities are not of the same type.

27. The method of claim 1, wherein each of the entities is of a type that is one of:

user, device, application, uniform resource locator (URL), session, or threat; and

wherein the activity includes at least one of;

uses, visits, connects to, logs in, or logs out.

28. A computer system for detection of an anomaly in a distributed computer environment, the system comprising:

a communication device; and

a processor configured to;

receive, by the computer system, raw machine data produced by an information technology environment, the raw machine data indicative of activity of one or more components of the information technology environment;

wherein the received raw machine data include a plurality of data units, wherein the raw machine data in each data unit of the plurality of data units includes data indicative of an activity, entities that participated in the activity, and a timestamp for the activity;

for each data unit of the plurality of data units, by the computer system,

identify a relationship between the entities indicated in the data unit, the relationship indicative of the activity indicated in the data unit, and

annotate, by using a data structure corresponding to a graph, the raw machine data in the data unit to incorporate data indicative of the relationship into the raw machine data in the data unit; and

provide, to an anomaly detection module, each of the plurality of data units including annotated raw machine data, for detection of a security-oriented anomaly in the information technology environment,

wherein the anomaly detection module is in a real-time path or a batch path, and wherein information regarding identified security-oriented anomalies is shared between the real-time path and the batch path.

29. A non-transitory machine-readable storage medium for use in a processing system for detection of an anomaly in a distributed computer environment, the non-transitory machine-readable storage medium storing instructions, an execution of which in the processing system causes the processing system to perform operations comprising:

receiving raw machine data produced by an information technology environment, the raw machine data indicative of activity of one or more components of the information technology environment;

wherein the received raw machine data include a plurality of data units, wherein the raw machine data in each data unit of the plurality of data units includes data indicative of an activity, entities that participated in the activity, and a timestamp for the activity;

for each data unit of the plurality of data units,

identifying a relationship between the entities indicated in the data unit, the relationship indicative of the activity indicated in the data unit, and

annotating, by using a data structure corresponding to a graph, the raw machine data in the data unit to incorporate data indicative of the relationship into the raw machine data in the data unit; and

providing, to an anomaly detection module, each of the plurality of data units including annotated raw machine data, for detection of a security-oriented anomaly in the information technology environment,

wherein the anomaly detection module is in a real-time path or a batch path, and wherein information regarding identified security-oriented anomalies is shared between the real-time path and the batch path.